Background: Scapulohumeral periarthritis commonly afflicts individuals in their middle age. Its etiology is multifaceted, and treatment presents a challenge with a high risk of recurrence. The main symptoms include shoulder pain and limited joint mobility, seriously affect the quality of life of the patients. Recent research indicate that acupuncture combined with bloodletting cupping can significantly improve the function of activity of shoulder joint and the pain in individuals with scapulohumeral periarthritis. However, these studies have typically been limited in scope, therefore additional research to substantiate the efficacy and safety of these intervention. Methods: To evaluate the efficacy of acupuncture combined with bloodletting cupping for treating patients with scapulohumeral periarthritis. We conducted an online search of databases in both Chinese and English, including PubMed, the Cochrane Library, Embase, Web of Science, CNKI, Wangfang Data, China Science and Technology Journal Database (VIP) and Chinese BioMedical Literature Database (CBM), to collect randomized controlled trials (RCTs) concerning the use of acupuncture combined with bloodletting cupping in scapulohumeral periarthritis patients. We also examined the references within the identified literature. Search utilised subject headings and free-text terms in both languages, without racial restrictions, for records up to April 3, 2024. Two researchers independently screened the literature, extracted data, and evaluated their qualities. RevMan 5.3 software was used for meta-analysis of the included studies. The protocol of this review was recorded in the International Platform of Registered Systematic Review and Meta-analysis Protocols (PROSPERO). Its registration number is CRD42023454614. Results: This review incorporated 22 RCTs involving a total of 1,774 patients. The results of meta-analysis showed that the clinical effective rate (RR=1.25, 95%CI [1.20, 1.30], P＜0.00001) of treating scapulohumeral periarthritis with acupuncture combined with bloodletting cupping was higher in the experimental group than in the control group. The all of Visual Analogue Scale (VAS) score (MD=-1.70, 95% CI [-2.17, -1.22], P＜0.00001). Melle score (SMD=-2.45, 95% CI [-2.55, -2.34], P=0.007]) and recurrence rate (RR=0.23, 95% CI [0.07, 0.77], P=0.02) were lower in the experimental group than in the control group with statistical significance (P<0.05). Conclusion: The acupuncture combined with bloodletting cupping for the treatment of shoulder impingement syndrome demonstrates definite efficacy and safety, with superior clinical effectiveness, pain relief, improvement in shoulder joint mobility, and reduction in recurrence compared to acupuncture alone. Therefore, it is worthy of being promoted and applied clinically.

Silver nanoparticles (Ag⁺NPs) contamination in the environment is a serious concern. This study investigated selected heavy metal (Ag⁺, Cd^2+, Cr²⁺ and Pb²⁺) concentrations at different sampling points to assess the risk to human health (infants, children, and adults). To do this, an enclosed area (laboratory) of 12.6 m X 8.5 m (107.1 m²) was clearly marked at different coded distances of S1, S2, S3, and S4 representing 2, 4, 6, and 8 m, while unpolluted atmosphere at 50 m away without Ag⁺NPs served as the control (S5). The silver fireworks were allowed to burn for an approximate 00h03m30s at each sampling points using a high-volume air sampler mounted at the Environmental Engineering Departmental Laboratory, Rivers State University, with windows and doors closed to simulate indoor conditions. Samples were digested using a mixture of analytical-grade nitric acid, analytical-grade hydrochloric acid and analyzed to evaluate the levels of heavy metals by atomic absorption spectrophotometry. The Ag⁺ result at S1 shows 30,000 µg/cm³, S2 was 29,000 µg/cm³, while S3 was 28000 µg/cm³ and then S4 was 13,000 µg/cm³. These results exceeded the permissible values of the United States National Ambient Air Concentration for rural, urban and industrial areas (0.0005, 0.004 and 0.6 µg/cm³, respectively). The result for the control (S5) (0.037 µg/cm³) was within the maximum allowable value. Results from other heavy metals such as Cd were 1000, 743, 401, 153, 0.001 µg/cm³, Cr was 5000, 4000, 3729, 2960, 0.002 µg/cm³, Pb was 0.048, 0.041, 0.035, 0.034 and 0.01, µg/cm³, respectively. However, higher values of Ag⁺, Cd, and Cr indicated a higher propensity for the metals to be toxic (bioavailable). In addition, the assessment of the potential health risk posed by these metals proved contaminated and harmful. Visitors recorded high values in exposure concentration (EC) and low values in average daily dose (ADD).

Background: Recently specific interactions and crosslinks between the gut microbiota and the lungs have been recognized, particularly with regard to respiratory immune and anti-microbial reactions. This is often known as the “gut-lung axis” or “a common mucosal immunological system”. Objective: The aim of the current systematic review was to evaluate evidence, from published clinical trials and cohort studies, if probiotics may have an effect in improving and managing COVID-19 symptoms. Materials and methods: The available studies were searched through a comprehensive search of electronic databases that included PubMed, Science Direct, Scirus, ISI Web of Knowledge, Google Scholar and CENTRAL (Cochrane Central Register of Controlled Trials), using a combination of the following keywords: “COVID-19" OR "SARS-CoV-2" AND "Microbiota" OR "Probiotics” OR “Gut Lung Axis”. The literature was reviewed until August 31, 2022. Results: Only 3 studies were included. One of them evaluated the efficacy of probiotics in COVID-19 patients to obtain complete remission of all signs and symptoms. The clinical trial proves that probiotics have a significant effect on complete remission of all signs and symptoms of COVID-19 patients with statistical significant difference. Only one clinical trial out of the 3 included studies had evaluated the need for O2 therapy during the study between the probiotics and control groups, but without statistical significant difference. No statistical significant difference between the probiotics group and placebo group was observed regarding fatal prognosis during the only clinical trial that measured death as an outcome. Conclusion: We couldn’t judge on these results as they are insufficient data for pooling and meta-analysis. However, what we can say is “Most probably Probiotics have no role in treatment of COVID-19 infection”.

Background: Coronavirus disease 2019 (COVID-19) is a newly emerging human disease caused by a novel coronavirus, causing a global pandemic crisis. Probiotics and/or colchicine may be considered as options for treatment since they have anti-viral, anti-inflammatory, and immunomodulatory effects. The aim of the current review was to assess the effectiveness of probiotic supplements and colchicine on symptoms, duration, and progression of mild and moderate cases of COVID-19 infection. Review: A randomized, double-blind, placebo-controlled trial in the United States with 182 participants who were randomly assigned to receive daily oral probiotic (Lactobacillus rhamnosus) LGG or placebo for 28 days. The study indicated that LGG is well-tolerated and is associated with a delay in the onset of COVID-19 infection, a reduction in the incidence of symptoms, and alterations in the structure of the gut microbiome when administered as post-exposure prophylaxis within seven days of exposure. Colchicine may lessen mortality and the need for mechanical ventilation in mild-to-moderate COVID-19 patients, according to a systematic review and meta-analysis. Conclusion: Probiotics and/or colchicine may be viable treatment options for COVID-19 patients. To examine the efficacy of probiotics and colchicine in the treatment of COVID-19, it is necessary to conduct additional clinical trials and provide clinicians with evidence, as there are currently insufficient studies to support this conclusion.

Background: The COVID-19 pandemic disrupted routine osteoporosis care due to clinic closures and limited in-person consultations. Telehealth emerged as an alternative model enabling remote care delivery and monitoring. However, previous reviews on telehealth either did not include the pandemic period or had a limited focus in scope. Evidence synthesized specifically for osteoporosis care during the pandemic is needed but lacking. Methods: We systematically searched PubMed, MEDLINE, EMBASE, PsycINFO, Web of Science, and CINAHL for studies on telehealth for osteoporosis published between January 2021 and March 2023. Five studies met the inclusion criteria of: osteoporosis population, telehealth intervention, and COVID-19 pandemic timeframe. Data was extracted on study characteristics, COVID-19 outcomes, osteoporosis status, telehealth purpose, patient satisfaction, and clinical outcomes. Result: The five studies showed telehealth was used for monitoring data, delivering test results, adjusting medications, and assessments. Osteoporosis prevalence among telehealth users ranged 30-100%. High patient satisfaction was reported with telehealth versus in-person care. No major differences occurred in medication delays or fractures between telehealth and in-person groups. Conclusion: This review found telehealth enables effective osteoporosis care and monitoring during the pandemic, with high patient and provider satisfaction. However, more robust randomized controlled trials are needed to establish stronger evidence around telehealth's impacts on clinical osteoporosis outcomes. Implications: Though promising, further high-quality studies will help clarify telehealth's role in improving osteoporosis care and outcomes. Findings inform guidelines on integrating telehealth into routine management. Evidence on user perspectives optimizes telehealth implementation policies.

Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.

Poor irrigation water quality due to oil spillage on surface water can result in food insecurity, health and economic challenges. This paper investigated the effect of total petroleum hydrocarbon (TPHC) and lead (Pb) on irrigation water quality in the oil spill prone area of the Niger Delta region of Nigeria. Water samples were taken from five different sections labelled A, B, C, D, and E along the Choba River, in Rivers State, Nigeria. Sections B, C, D and E were direct industrial effluent discharge points while section A was without direct industrial effluent discharge. Standard methods were employed in the water sampling and analysis. Suitability of Choba river water for irrigation was assessed by comprehensive pollution index (CPI) that incorporated salinity, sodicity, and permeability hazard potentials as well as the specific toxicity hazard potentials of TPHC and Pb. Results showed that all primary water parameters except pH were within the Food and Agriculture Organization (FAO) guidelines. The pH was low, ranging between 4.48 and 5.6. TPHC values for four out of the five samples were greater than the 10mg/l guideline as recommended by the Directorate of Petroleum Resources for surface water. TPHC for the four samples ranged between 14.52 and 174.32mg/l. The parameters with the most impact on CPI include EC, PI and TPHC with TPHC having the most impact. Water samples from sections A, B and E with CPI values 0.14, 0.37, and 0.8 respectively were classified in the clean, sub clean and slightly polluted categories respectively, while water samples from sections C and D with CPI values greater than 1 range from moderately to heavily polluted and not suitable for agricultural irrigation. Only water sample A was found suitable for irrigation.

Objectives: There remains no consensus management for isolated distal deep vein thrombosis (IDDVT), with current data inconclusive and dating back to the warfarin era. In the current direct oral anticoagulant (DOAC) era, optimal management of IDDVT needs to be re-assessed. Methods: A retrospective evaluation of patients treated with therapeutic anticoagulation for IDDVT in the DOAC era (2013-2016) was compared with historically published data from the warfarin era (2011-2012). Results: 247 patients were evaluated, 103 from the DOAC era and 122 from the warfarin era. There were less provoked events in the DOAC cohort (45.6% vs 66.7%, p=<0.01). Overall rate of major bleeding was 1.6% with 1.0% in the DOAC era and 2.1% in the warfarin era (p=0.50). There was no difference in rates of VTE progression on treatment 5.8% vs 4.9% respectively (p=0.91). Overall risk of VTE recurrence post cessation was 5.3% (1.86 per 100 person years) with no difference between groups (5.8% vs 4.9%, p=0.74). Conclusions: Our data shows IDDVT is not always benign, with risk of extension despite treatment and long-term risk of VTE-recurrence. Therapeutic anticoagulation with DOAC in these patients was associated with a major bleeding rate of 1.0% in the DOAC cohort. Further clinical trials into the optimal IDDVT management in the DOAC era are necessary.

Early-onset neurodegenerative diseases (EO-NDs), such as early-onset Alzheimer’s disease (EOAD), Parkinson’s disease (EOPD), and familial amyotrophic lateral sclerosis (fALS), often stem from monogenic causes and manifest before typical age thresholds. These disorders frequently feature disrupted mitochondrial function and heightened oxidative stress, which together accelerate neuronal damage and degeneration. In this work, the author performs a comprehensive analysis of the literature and data related to mitochondrial dysfunction and redox imbalance in EO-NDs. Bibliometric trends were assessed using R-based tools on PubMed datasets, highlighting keyword networks and publication surges in recent years. Publicly available RNA-seq datasets from GEO and SRA were examined, with example DESeq2 analysis illustrating altered mitochondrial gene expression in EO-ND patient-derived samples. Network modeling of redox pathways using Python’s networkx demonstrates how oxidative stress can propagate through metabolic networks. Together, these computational approaches reinforce that mitochondrial DNA mutations, impaired electron transport chain (ETC) function, and reactive oxygen species (ROS) accumulation play central roles in EO-ND pathogenesis. The discussion further evaluates why antioxidant clinical trials have largely failed and how emerging therapies such as gene replacement, antisense oligonucleotides, and mitochondrial biogenesis modulators may provide more effective interventions.

This paper is a general summary of Neuralink, a revolutionary technology set to elevate human life and neurology. Neuralink itself is a key testimonial to the evolution of neuroscience and even brain-computer interfaces, otherwise known as BCI. The original few BCI experiments were conducted on monkeys in the 1960s and 70s, in which the experiment itself narrowed down and understood brain function as a general concept [3]. More specifically, "Work on these technologies began in the early 1970s, led by computer science professor J.J. Vidal at UCLA" [12]. Science itself progresses day by day, growing rapidly in recent years, especially in neuroscience, something highlighted as a focal point in the previous statement. Moreover, recently we have seen technology go on a rampant rise in terms of popularity, inventions, and changes to the human lifestyle. The interactions humans had with technology initially developed with wearables or wearable technology, such as Apple Watches, AirPods, and Fitbits, and now they have even prompted advancements in brain-computer interfaces. Technology has had the power to advance science, but now it’s capable of changing the human mind. Going back to Neuralink, it’s a startup that began its initiative in 2016 and was approved by the FDA for clinical trials in May of 2023, ready to create a wave of change in the field of neuroscience [6]. The foremost baffling thing is how this chip plans on being placed in the somatosensory system. The somatosensory system is a part of the brain that deals with motor actions, recognition, and perception, and applying Neuralink in this area should supposedly allow for cures and treatment of amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injuries, epilepsy, autism, depression, schizophrenia, and possibly blindness [9]. Neuralink is deemed to lead to a life-changing future, and with co-founders and investors like Elon Musk, there is a lot to know about this piece of technology.

Objective: This systematic review aimed to identify, synthesize, and critically analyze the available evidence on clinical protocols used for the prevention and treatment of prosthetic joint infection (PJI) in total hip arthroplasty (THA), based on studies published between 2000 and 2025. Methods: The review was conducted according to PRISMA guidelines. Electronic searches were performed in PubMed (MEDLINE), Scopus, Web of Science, and Embase between January and April 2025. Eligible studies included clinical trials, cohort studies, case-control studies, systematic reviews, and meta-analyses published in English that addressed either preventive or therapeutic strategies for PJI in THA. Study selection, data extraction, and quality assessment were carried out independently by two reviewers. Due to the heterogeneity of the included studies, a qualitative synthesis was performed. Results: A total of 32 studies were included. Preventive measures identified in the literature comprised combined antibiotic prophylaxis (cefazolin and gentamicin), multimodal perioperative protocols such as ACERTO, nasal decolonization for Staphylococcus aureus, silver-impregnated dressings, and structured post-discharge surveillance. Treatment strategies included DAIR (Debridement, Antibiotics, and Implant Retention), the DAPRI technique, one-stage and two-stage revision surgeries, muscle flap reconstructions, and protocols without spacers. These interventions were associated with significantly reduced infection rates and improved clinical outcomes when applied appropriately and in accordance with patient-specific factors. Conclusion: Effective prevention and treatment of PJI in total hip arthroplasty require a systematic and evidence-based approach. Integrated protocols—spanning preoperative optimization, meticulous intraoperative techniques, and rigorous postoperative monitoring—have proven effective in reducing infection incidence. In cases of established infection, surgical management must be tailored to the timing of infection, microbial profile, and host conditions. Two-stage revision remains the gold standard for complex infections, while one-stage revision and emerging techniques like DAPRI offer promising results in selected cases. This review contributes to the standardization of clinical practice and supports improved patient outcomes.

This systematic review aimed to evaluate the biomechanical properties, functional performance, and clinical outcomes of different hip prosthesis materials and designs used in total hip arthroplasty (THA). A comprehensive search strategy identified 34 peer-reviewed studies published between 2015 and 2024. The materials investigated included cobalt-chromium-molybdenum (CoCrMo), titanium alloys, PEEK, ceramics, and advanced surface coatings such as polycrystalline diamond (PCD). In addition, dual mobility systems, lattice structures, and additively manufactured and patient-specific implants were assessed. The studies utilized clinical trials, finite element analysis, and biomechanical testing to compare outcomes such as wear resistance, stress distribution, osseointegration, and range of motion. The findings demonstrated that titanium alloys and porous lattice structures reduce stress shielding, while ceramics and CoCrMo provide superior wear resistance. Dual mobility implants improved joint stability and range of motion, particularly in high-risk patients. PEEK and PCD showed promising properties but lacked robust long-term data. The integration of advanced manufacturing technologies and material innovations has led to more personalized and biomechanically efficient solutions for THA. Further longitudinal studies are needed to validate these developments. This review provides a critical synthesis of the biomechanical, functional, and clinical implications of contemporary hip prosthetic systems.

Background: Childhood poverty is a critical determinant of developmental, health, and behavioral outcomes. However, racial and ethnic differences in how families experience and navigate poverty suggest that a one-size-fits-all approach may not be an effective approach for alleviating disparities. Understanding baseline demographic, socioeconomic, health, and behavioral characteristics among families in poverty is crucial to designing equitable interventions. Objective: To examine racial and ethnic differences in baseline demographic, socioeconomic, health, and behavioral characteristics among families living in poverty, using data from the Baby's First Years (BFY:2018-2019) Study. Methods: This analysis used baseline data from the BFY study, a randomized controlled trial (RCT) designed to evaluate the effects of monthly cash assistance on children’s developmental outcomes. The sample included 1,050 children and their families, consisting of mothers and, when available, fathers. Descriptive analyses were conducted to compare demographic, socioeconomic, health, and behavioral outcomes across racial and ethnic groups, focusing on Black, Hispanic, and other mothers. Results: Significant racial and ethnic differences were observed. Regarding demographic factors, Hispanic mothers were older than White mothers. In terms of socioeconomic factors, Hispanic mothers had fewer years of education compared to White mothers, while Black mothers were more likely to receive food stamps than mothers from other groups. Regarding health and behaviors, Black mothers reported worse self-rated health but were less likely to plan for breastfeeding compared to White mothers. In contrast, Hispanic mothers reported lower levels of depression and were more likely to plan for breastfeeding. Conclusion: These findings highlight the heterogeneity of experiences among families living in poverty, with significant differences across racial and ethnic groups. Such disparities underscore the importance of tailoring anti-poverty policies to the unique needs of diverse populations. Future research should explore how the effects of interventions, such as cash assistance or guaranteed income, may differ across racial and ethnic groups to inform equitable and effective policy development.

Background: Menthol-flavored tobacco products are disproportionately used in low-income African American communities, a result of decades of targeted marketing and systemic inequities. Menthol use has been associated with lower quit rates, often compounded by factors such as lower trust in healthcare systems, reduced access to cessation programs, and other structural barriers. Despite this, few studies have systematically examined the explanatory mechanisms that might clarify why menthol-flavored tobacco is linked to poorer cessation outcomes among participants in tobacco cessation programs. Aims: This study aimed to investigate the potential mechanisms by which menthol tobacco use is associated with lower quit rates across three types of smoking cessation interventions. Methods: Participants were randomized into one of three smoking cessation interventions: in-person (CEASE), self-help, or online/hybrid programs. Smoking abstinence was assessed three months post-intervention as the primary outcome. Secondary analyses explored whether demographic, socioeconomic, or behavioral factors mediated the association between menthol use and quit rates across the intervention arms. Results: Menthol tobacco use was significantly associated with lower quit rates (p < 0.01). This association was not explained by demographic, socioeconomic, health, or addiction-related factors. While menthol use was associated with lower education and employment levels, demographic characteristics, physical or mental health, or addiction did not explain the effect of menthol on tobacco cessation. These findings suggest that the lower quit rates observed among menthol users cannot be attributed to any third factors assessed in this study. Conclusions: Menthol tobacco use independently predicts lower quit rates, and the mechanisms behind this disparity remain unclear. The consistent findings across different intervention types highlight the need for further research to uncover the underlying pathways and to design targeted strategies to improve cessation outcomes for menthol users.

Background: Tobacco use remains disproportionately high among low-income and racial-ethnic minority populations. The CEASE program, with its self-help, hybrid/online, and in-person modalities, has demonstrated efficacy in promoting tobacco cessation. However, predictors of successful cessation among participants in these groups remain unclear. Objective: To identify baseline predictors of successful tobacco cessation among low-income and racial-ethnic minority participants in the CEASE program, with a focus on demographic, socioeconomic, behavioral, and psychosocial factors. Methods: Participants were allocated into three intervention arms: self-help, CEASE hybrid/online, and CEASE in-person. Baseline characteristics, including demographics (e.g., age, gender), socioeconomic status (e.g., education, employment), substance use profiles (e.g., cigarette packs per week, use of other tobacco products, menthol tobacco use), physical health (e.g., general health, number of cardiometabolic risk conditions), mental health (e.g., depressive symptoms, perceived stress), perceived social support, and nicotine dependence, were analyzed as potential predictors of cessation success. Multivariable logistic regression models were used to identify factors associated with successful quitting, controlling for the study arm. Results: In addition to the study arm, gender, baseline depression, cardiometabolic conditions, tobacco flavor, and the use of other tobacco products were significant predictors of quit success. Individuals receiving in-person interventions had significantly higher odds of quitting (AOR = 3.79, p < 0.05). Women were significantly less likely to quit compared to men (AOR = 0.24, p < 0.01). Participants with a greater number of cardiometabolic risk conditions were more likely to quit (AOR = 1.93, p < 0.05), while those with higher levels of depression had lower odds of quitting (AOR = 0.61, p < 0.05). Menthol tobacco users were also less likely to quit (AOR = 0.10, p < 0.05). Interestingly, individuals who used other forms of tobacco in addition to cigarettes had increased odds of quitting (AOR = 2.86, p < 0.05). No other factors, including demographic variables (e.g., age), socioeconomic status (e.g., education, marital status), substance use profiles (e.g., cigarette packs per week, NRT use), or nicotine dependence, were significant predictors of cessation success. Conclusion: Baseline self-reported anxiety/depression and depressive symptoms play a critical role in reducing the likelihood of successful tobacco cessation among low-income and racial-ethnic minority participants in the CEASE program. These findings underscore the importance of addressing mental health challenges as part of tobacco cessation interventions to enhance their efficacy. Future research should explore targeted strategies for integrating mental health support into cessation programs to improve outcomes for underserved populations.

Active Pharmaceutical Ingredients (APIs) serve as the cornerstone of pharmaceutical development, driving therapeutic efficacy and safety in drug formulations. This article provides a comprehensive overview of the lifecycle of APIs, starting from their discovery and development, through to manufacturing processes and regulatory oversight. The development of APIs begins with intensive research and discovery efforts, where medicinal chemists and pharmacologists identify and optimize potential compounds through computational modelling, high-throughput screening, and structure-activity relationship studies. Promising candidates undergo rigorous preclinical testing to assess pharmacological properties, safety profiles, and potential adverse effects in animal models. Upon successful preclinical outcomes, APIs progress to clinical trials, involving phases of testing in human subjects to evaluate efficacy, dosage regimens, and safety profiles under controlled conditions. Clinical trial data are meticulously analyzed to support regulatory submissions, demonstrating the API's therapeutic benefits and safety for eventual patient use. Manufacturing APIs involves complex chemical synthesis or biotechnological methods, ensuring precise control over reaction conditions, purity, and yield. The scale-up from laboratory synthesis to industrial production demands adherence to Good Manufacturing Practices (GMP), where stringent quality control measures verify consistency, potency, and stability throughout production batches. Regulatory oversight by authorities such as the Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in Europe ensures that APIs meet stringent standards of safety, efficacy, and quality before market approval. Manufacturers must submit comprehensive Chemistry, Manufacturing, and Controls (CMC) data, detailing manufacturing processes, analytical methods, and stability studies to support regulatory filings.

The purpose of this study was to assess different periodontitis groups affected with mild, moderate and severe alveolar bone levels and treated using the Sandwich’s procedures. A total of 53 subjects, who had taken two sets of full-mouth standarized paralleling radiographs with mean observation time was 10.18±3.89 years and (ranges: 5.1 to 18.3 years) were collected for the past 20 years. The radiographic alveolar bone levels (RABL) at mesial and distal aspects of teeth were assessed by measuring the distance between cemento-enamel junction (CEJ) and the bone crest using an electronic digimatic caliper (EDC) under 7.5 magnified radiographs. The patients, who presented with SAP, were between 24 and 84 years of age, with a mean age of 54.8±10.2years. Although, the treatment of angular defects in molars with guided tissue regeneration, emdogain has been reported and has exhibited significant and predictable results, however, afforded very limited and less predictable results in the treatment of advanced class II and III maxillary furcation defects. The majority of root resection procedures are commonly recommended for treating advanced molar furcation, in particular located at molars with class III furcation involvement, there is still some controversy regarding the long-term prognosis after different treatment modalities. In general, the root resection procedure is a surgical approach for simultaneously performing a periodontal flap operation at first and followed by the amputation and/or resection of maxillary root(s). There are some complications and disadvantages, such as post-operative pain and bleeding, swelling, infection, etc. The present report is to describe the combination of therapeutic provisional prosthesis (TPP), fixed prosthesis, non-surgical procedure using root separation and/or resection (RSR) and for the treatment of advanced Class II and III furcation-Involved molars. In addition, evidenced-based clinical trials of retrospective and longitudinal data were also prescribed here. The purpose of this study was to present treatment procedures of Sandwich’s technique and retrospectively to evaluate the long-term clinical trials of this method in treating molar teeth with SAP and molar FI who were diagnosed as guarded and/or hopeless prognosis.

Myeloproliferative neoplasms (MPNs) are hematological disorders affecting myeloid stem cells. They are classified as Philadelphia (Ph) chromosome positive-chronic myeloid leukemia, and Ph-negative polycythemia vera, essential thrombocythemia, primary myelofibrosis, chronic neutrophilic leukemia, chronic eosinophilic leukemia, juvenile myelomonocytic leukemia, and MPN unclassifiable. This review is mainly focused on the Ph-negative MPNs namely, PV, ET, and PMF. These affect both males and females with a slight male predominance, with patients mainly presenting in the seventh decade. Patients often present with thrombotic events resulting in complications that lower survival rates. The major driver mutations that have been identified in MPNs are JAK2 Exon 14, JAK2 Exon 12, MPL Exon 10, and CALR Exon 9. The importance of these driver mutations gives due recognition to their inclusion into the 2022 diagnostic criteria of the MPN WHO Classification. However, other non-driver mutations have also been reported, especially in triple-negative cases. These mutations lead to downstream constitutive activation of the JAK/STAT signaling pathway, as well as the MAPK, and PI3K/Akt pathways. Insights into the molecular pathogenesis of MPN and its association with JAK2, CALR, and MPL mutations have identified JAK2 as a rational therapeutic target. Thus, as an approach to MPN therapy, JAK2 inhibitors, such as ruxolitinib, have been shown to effectively inhibit JAK2, and are currently in clinical trials in combination with other drug classes. This review comprehensively examines the molecular markers of the main Ph-negative MPNs, as well as diagnosis and treatment options.

PLCs, or programmable logic controllers, are essential parts of contemporary industrial automation systems and are responsible for managing and keeping an eye on a variety of operations. PLC reliability is critical to maintaining industrial systems' continuous and secure operation. A wide range of reliability strategies were used to improve the reliability of Programmable Logic Controllers, and this article methodically looks at them all. The evaluation classified PLC reliability techniques into Root Cause Analysis (RCA), Reliability Centered Maintenance (RCM), Hazard analysis (HA), Reliability block diagram (RBD), Fault tree analysis (FTA), Physics of failure (PoF) and FMEA/FMECA, after thoroughly reviewing the body of literature. The proportion of reviewed papers using either RCA, RCM, FMEA/FMECA, FTA, RBD, RCM, PoF, or Hazard analysis to increase the reliability of PLCs showed that RCA, which makes up 20% of the publications reviewed, has been used the most to increase the reliability of the PLC, followed by HA, RCM, RBD, FTA, and PoF, which account for 17%, 16%, 16%,13%, 10%, and 8% of the articles reviewed, respectively. The paper discusses new developments and trends in PLC reliability, such as the application of machine learning (ML) and artificial intelligence (AI) to fault detection and predictive maintenance.

The control of processes is made smooth and effective by Programmable Logic Controllers (PLCs), which are essential to industrial automation. The assessment of PLCs' reliability is crucial since more and more sectors depend on them for crucial tasks. In-depth reviews of the components necessary to evaluate PLC system reliability are presented in this study. To ensure a robust review, the review first clarifies the basic concepts of reliability, highlighting the significance of system uptime and the ramifications of failures in industrial settings. Next, it examined the different elements that go into a PLC's overall reliability, such as availability, testability, and (maintenance and maintainability). The percentage of the reviewed papers that employed (maintenance and maintainability), testability, or availability to improve the reliability of PLC systems showed that, availability and (maintenance and maintainability) has been employed the most for enhancing system reliability, accounting for 32% each of publications analyzed, followed by testability, accounting for 28% respectively. The scatter chart that depicts the progression of reliability components from 2010 to 2023 also explained that the use of availability and (maintenance and maintainability) was increasing. This upward trend can be explained by the fact that repairable systems are heavily reliant on availability, whereas (maintenance and maintainability) tend to avoid unnecessary equipment breakdown and testability, which ensures the ease with which the functionality of any system or component can be ascertained with the required level of precision.

An increasing number of adverse events are raising concern in the pharmaceutical supply chain due to contaminated active pharmaceutical ingredients (APIs). Most of the active pharmaceutical ingredients are not currently under the scope of environmental regulations, despite their negative impact on human health and the environment. API's life cycle plays a significant role in identifying potential supply chain sources and determining their impact on the environment. The Covid-19 pandemic's intermittent manufacturing interruptions and the increase in the frequency of drug shortages over the past ten years have sparked worries about how resilient the world's drug supply chains are. Many clinical trials were conducted on patients with COVID-19 during the SARS-CoV-2 pandemic and resulted in millions of deaths globally by 2022.

Thanks to their interesting mechanical properties, recyclability and low production costs, plant fiber-reinforced composites, derived from agricultural residues, are of particular interest to both manufacturers and scientists looking to incorporate new environmentally-friendly and biodegradable materials to replace synthetic fibers, particularly glass fibers. The growing use of these composites in fields such as the automotive, construction and building industries, and soon in aeronautics, raises concerns about the reliability of the structures with which they are manufactured. This reliability must be guaranteed at the design stage, by a good knowledge of the properties of the material used. In this case, for composites, it is necessary to know the mechanical properties of their constituents, fibers and matrix, etc. In this context, this paper focuses firstly on the economic and industrial recovery of Kenaf (K) and Date Palm (DP) fibers, and secondly on their incorporation as a reinforcing element in cementitious matrix composites, for subsequent use in non-structural applications. This research highlights the development of cementitious matrix bio-composites reinforced with this type of fiber, based on Taguchi's statistical methodology, in order to minimize the cost and number of tests. The bio-composites developed are then mechanically characterized under static loading in compression and 3-point bending after a 30-day drying period.

The technologies for cloud ERP (Enterprise Resource Planning) have revolutionized the field of information technologies. Any kind of business can benefit from their flexibility, affordability, scalability, adaptation, availability, and customizable data. An advancement of classic ERP, cloud enterprise resource planning (C-ERP) provides the benefits of cloud computing (CC), including resource elasticity and ease of use. The rise of cloud computing affects on-premise ERP systems in terms of architecture and cost. Cloud-based ERP systems make the claim to be appropriate for digital corporate settings. System quality, security, vendor lock-in, and data accessibility are recognized as the technological issues. Industry 4.0 refers to the re-engineering and revitalization of modern factories through the integration of cloud-based operations, industrial internet connectivity, additive manufacturing, and cybersecurity platforms. One of the four main pillars of Industry 4.0, cloud-based Enterprise Resource Planning (Cloud ERP), is a component of cloud operations that aids in achieving greater standards of sustainable performance.

The role of enterprise applications in pharmaceutical industries is driving the digital transformation of various critical processes, and one process benefiting from this innovation is pharmaceutical drug traceability. This industry grapples with challenges like a lack of transparency, difficulties in tracking products, a deficit of trust, and issues related to shipping expired products. To address these concerns, blockchain technology as an enterprise application has been harnessed as a solution. Notably, counterfeit drug prevention emerged as the most prevalent category, aligning with the pharmaceutical industry's primary objective. Blockchain technology is an emerging innovation that is finding enterprise applications in various industries, including healthcare. In the healthcare sector, Blockchain networks are being utilized to securely store and exchange patient data across hospitals, diagnostic laboratories, pharmacies, and medical practitioners. These enterprise applications can effectively identify and mitigate critical errors, including potentially hazardous ones within the realm of healthcare. Consequently, this enterprise technology holds the promise of enhancing the efficiency, security, and transparency of medical data sharing within the healthcare system. Moreover, it offers valuable tools for medical institutions to gain insights and improve the analysis of medical records. It visually represents the diverse capabilities, enablers, and the unified workflow process of Blockchain technology in supporting healthcare on a global scale. Additionally, the paper presents a thorough discussion of fourteen significant applications of Blockchain in healthcare, underscoring its pivotal role in addressing issues like deception in clinical trials.

Quantitatively, this study aimed to determine the abundance and diversity of the insect fauna that visits the staminate and pistillate flowers of Cocos nucifera. The study was conducted at an experimental plantation belonging to the Coconut Research Programme (CRP) of the Oil Palm Research Institute (OPRI) of the Council for Scientific and Industrial Research (CSIR), to provide diagnostic support for the Cape St. Paul Wilt Disease (CSPWD) at Asebu in the Central Region of Ghana. The populations of coconut palms represented the dwarf type with few tall ecotypes. Five Insects were randomly chosen with newly opened inflorescences. Observations and collections of insect visitors to coconut flowers were made once a week on 30 newly opened inflorescences, five from each batch within the plantation. Specimens of the data were deposited in the official insect collection and processed at the laboratory of the Entomology Museum of the Department of Conservation Biology and Entomology, University of Cape Coast, Ghana. The study indicated that 9 different species of insects were identified to be the true fauna that visited the staminate and pistillate flowers of C. nucifera Ethiosciapus sp., Sarcophaga sp., Scolia dubia, Lucilia sp., Ornidia sp., Apis melifera, Dactylurina standingeri, Red Ant and Black Ant. These insects were observed in all the six batches considered and were available at all times of the day. Most of the insects were observed in the early morning from 6 am - 9 am followed by the evening 4 pm –7 pm. The abundance of insect visitors was low during the mid-day (11 a.m. to 3 p.m.) in all six batches during high temperatures. The results of this study revealed that there were abundances of Ethioscipus sp. was the least abundant in all the batches followed by Scolia dubia then Sarcophaga sp. Red Ants had the highest abundance in most of the Batches thus becoming the most abundant insect that forage the coconut inflorescence at the Asebu plantation. The bees, Apis melifera and Dactylurina standingeri were the most abundant species after the Red Ants. All these groups of insects were not considered in the study and it is recommended that further studies consider such visitors to observe which insects are doing what on the inflorescence. The range for the ‘time of day for’ of the study was mostly diurnal (morning 6 am-9 am, afternoon 11 am-2 pm and evening 4 pm7 pm). There was no observation made of the pollination system or activities of these insect visitors nocturnally. There may be high pollination activities of these insects during the late evenings. It is recommended that future work should incorporate the late evening period to observe an abundance of diurnal insect visitors of the coconut inflorescences.Keywords: Insect, Fauna, Staminate, Pistillate Flowers, Cocos nucifera

Pharmaceutical drug traceability is a regulatory compliance adopted by most nations in the world. A comprehensive analysis was carried out to explain the benefits of adopting enterprise system for pharmaceutical drug traceability. Counterfeit drugs are medicines that are fake and have been produced using incorrect potency, or incorrect ingredients used to manufacture these drugs. Solving the drug counterfeiting problems by identifying the most effective and innovative technologies for protecting people's health is of essence these days for the world. Drug serialization is essential concept for drug traceability in the pharmaceutical supply chain. Blockchain is the latest stringent technology that makes drug distribution more secure in the supply chain. The blockchain-based drug traceability is a distributed shared data platform that shares information that is irreversible, reliable, responsible, and transparent in the PSC. Blockchain uses two powerful module, Hyperledger Fabric and Besu to satisfy important criteria for medication traceability, such as privacy, trust, transparency, security, authorization and authentication, and scalability. Researchers in Health informatics can use blockchain designs as a useful road map to develop and implement end-to-end pharmaceutical drug traceability in the supply chain to prevent drug counterfeiting. Industrial IoT is also a key component for the pharmaceutical industry. IoT systems in pharmaceutical drug traceability can be beneficial as they are based on automation and computational methodologies.

Psoriasis is a multi-systemic chronic autoimmune inflammatory disorder affecting 125 million people worldwide. The most common type of psoriasis is plaque psoriasis affecting up to 90% of the patients and is characterized by well-demarcated, symmetric, and erythematous plaques with overlying silvery scales that may be painful or itchy. Psoriasis may also affect the joints; increase the risk of developing metabolic syndrome, diabetes, Crohn’s disease, ulcerative colitis, uveitis, certain cancers and an increase in the risk of cardiovascular diseases. Both the skin and the gut microbiome can modulate the development and progression of psoriasis. A connection between the microbiome and immunological mechanisms are antimicrobial peptides, which regulate the microbiome at interfaces and, as antigens, can trigger psoriasis. Few studies were conducted to demonstrate the effect of probiotics on different diseases, as they are living microorganisms that confer a health benefit when administrated in adequate amounts. The effects of administering probiotics include the stabilization of the gut bacterial community and the restoration of “signature” of bacterial microbiota, which is a result of lowering the pH, producing bacteriocins, altering microRNA (miRNAs), competing with pathogens for certain nutrients and improving the gut barrier function. Probiotics counter weight aggressive commensals in the body and reinforce the barrier function of the epithelium while also contributing to the regulation of innate and adaptive immune responses of the host under healthy or pathogenic conditions. Several clinical trials were conducted based on those findings to examine the role of probiotics in psoriasis, but till now there is no evidence of their efficacy.

Probiotics as an intestinal microbe regulator, not only improve the ability of the gastrointestinal microbiota to modulate immune activity, but also strengthen the body's immune system, inhibit allergic reactions and has a significant role especially in the anti-viral immunomodulation. Therefore, in patients with COVID-19, the intestinal micro-eco-regulator, represented by probiotics, may be a therapeutic choice. However, there is still a lack of evidence-based studies to support probiotic treatment of patients with COVID-19. New cohort studies and randomized controlled clinical trials to assess the effectiveness of probiotics in the management of COVID-19 are strongly and urgently needed.

The kinetics, equilibrium and thermodynamic study of the adsorption of Ni²⁺, Pb²⁺ and Cu²⁺ions from aqueous solution by the leaf of Mangiferaindica were investigated at different experimental conditions. Optimum conditions of initial metal ion concentration, pH, adsorbent dose, contact time and temperature were determined. The kinetics studies indicate that the adsorption process of the metals ions followed the pseudo second-order model with R² value of 0.9938, 1.00 and 1.00 respectively. Equilibrium studies showed that the adsorption of Ni²⁺, Pb²⁺ and Cu²⁺ ions are well represented by both Langmuir and Freundlich isotherm but the Langmuir model gave a better fit for Pb²⁺ ions with R² value of 0.9950 and Langmuir constant K_L of 4.3383 while Freundlich isotherm model best fit the experimental data of lead(II) and nickel(II) with a R² value of 0.976 and 0.9973 and Freundlich constant K_F value of 4.2677 and 0.0874. The calculated thermodynamics parameters of Ni²⁺, Pb²⁺ and Cu²⁺ ions are ( ΔG^o -1182.49,-5479.1 and 613.48 KJ/mol) showed that the adsorption of Ni²⁺ and Pb²⁺are spontaneous while Cu²⁺ non-spontaneous. The findings indicate that the leaf of Mangiferaindica could be used for the adsorption of Ni²⁺, Pb²⁺ and Cu²⁺ ions from industrial effluents.

The pandemic corona virus disease 19 (COVID-19), caused by (SARS-CoV-2) a single stranded-RNA virus, has been spread rapidly worldwide with high rate of morbidity and mortality. Few months after the spread of the pandemic, few medications have proven to be efficient in human clinical trials. Several antiviral drugs have been used outside the scope of their initial medical use, such as lopinavir, hydroxychloroquine or azithromycin. Recent researches were done to show the efficacy of ivermectin in reducing SARS-CoV-2 viral RNA within 2 days. The use of ivermectin in in vitro studies has proven its efficacy against Corona virus. Based on the potency of ivermectin in in vitro studies, various clinical trials including patients infected with COVID-19 have been started; most of them have not been completed yet. Since the way how the virus infects the cells in vitro and in vivo is different, a decisive comment about how the ivermectin could exactly be beneficial to the patients has not been proven yet. Nevertheless, if ivermectin is compared to the other therapeutic treatments available for COVID-19 management, ivermectin has proved to have leverage over them. New randomized controlled clinical trials to assess the effectiveness of ivermectin the management of COVID-19 are strongly and urgently needed.

The deflection of the vertical is an important parameter that combines both physical (astronomic) and geometric (geodetic) quantities. It is critical in such areas as datum transformation, reduction of astronomic observation to the geodetic reference surface, geoid modelling and geophysical prospecting. Although the deflection of the vertical is a physical property of the gravitational field of the earth; which almost all terrestrial survey measurements, with the exception of spatial distances, made on the earth surface are with respect to the Earth’s gravity vector, because a spirit bubble is usually used to align survey instruments. It has been ignored in most geodetic computation and adjustment. This research work is therefore aimed at computing the component of the deflection of the vertical component for part of Rivers State using a geometric method. This method involves the integration of Global Positioning System (GPS) to obtain the geodetic coordinate of points, precisely levelling to obtain the orthometric height of this point located within the study area. By least square using MATLAB program, the estimated deflections of vertical component parameters for the test station SVG/GPS-002 were; -0.0473” and 0.0393” arc seconds for the north-south and east-west components respectively. The associated standard errors of the North-south and East-west components were ±0.0093” and ±0.0060” arc seconds, respectively. The deflection of the vertical was also computed independently from gravimetric models of the earth as: ξ = 0.0204” ±0.0008814”, η = -0.0345” ±0.0014”; ξ =0.0157” ±0.000755”, η = -0.0246” ±0.0012”; ξ = -0.0546±0.0006014, η = -0.0208±0.0006014 for EGM 2008, EGM 1996 and EGM 1984 respectively. The two-tailed hypothesis test reveals that the estimated deflection component is statistically correct at 95% confidence interval. It was observed that the effect of the deflection of the vertical is directly proportional to the distance of the geodetic baseline. Therefore, including the derived component of deflection of the vertical to the ellipsoidal model will yield high observational accuracy since an ellipsoidal model is not tenable due to its far observational error in the determination of high-quality job. It is important to include the determined deflection of the vertical component for Rivers State, Nigeria.

In Central Africa, mushrooms are critically important non-timber forest products (NTFPs), both nutritionally and economically. A strain of edible and medicinal lignicolous fungus, Pleurotus tuber-regium (Rumph. ex Fr.) Singer 1951 (strain 190212), isolated from tissue (sclerotia), on PDA medium, was tested on corn grain and sawdust seedling substrates and on palm oil male inflorescence (Elaeis guineensis Jacq.), ground corn (Zea mays L) stalks and grass (Paspalum notatum L) soaked for 24 hrs then drained for 24 hours, and unsoaked ground corn (Zea mays L) stalks. The highest mycelial growth rate recorded was about 0.9 cm on the PDA medium; 5.97 cm on the corn-based seedling medium and 11.95 cm on the sawdust-based seedling medium. Total mycelial invasion on the PDA medium was observed on day 10, day 14 on the corn-based seedling medium, and day 24 on the sawdust-based seedling medium. The onset of mycelial invasion was noticeable on day 3 of seeding for all treatments T₀ (control), T₁ (Final substrate based on soaked ground corn stalks), T₂ (Final substrate based on unsoaked ground corn stalks), and T₃ (Final substrate based on turf). Total invasion of mycelium was obtained at day 15 of incubation for treatments T₁ and T₂, at day 18 for treatment T3 and at day 24 for treatment T0. The results obtained on treatments T₁ and T₂ respectively (14.95±3.12% and 15.65±1.06%) of the maize stalk substrate, lead us to believe that the strain 190212 of Pleurotus tuber-regium species used has adapted and requires an improvement of the medium with nitrogen-rich additives such as soybean meal. This could achieve the theoretical yield of 20% or more, according to which a substrate can be considered better in producing sporophores.

Alterations in the cardiac electrical system are the cause of morbidity and a wide variety of symptoms – from mild to those requiring urgent intervention – because of the risk of sudden cardiac death. The most prevalent of these types of electrical disturbances is atrial fibrillation, the incidence of which is growing as the average age of the human being increases. Among many other electrical heart alterations is one I describe in this document: "The presence of short PR and QTc intervals together in the same ECG tracing”. Methods: A cardiac calibrator was used by four different cardiologists in blinded fashion to document a distinct ECG pattern, that of a short PR and QTc intervals together in the same ECG tracing from more than 2.500 cases assessed for this condition (more than two thousand five hundred cases evaluated since 2.007 to date). Results: Here we describe the clinical features of 330 patients with a documented short PR and QTc intervals together in the same ECG tracing along with descriptions of their symptoms and ancillary investigations. Conclusions: ECG tracing must be studied carefully in patients with suggestive symptoms before declaring normal an ECG tracing with certain defined characteristics.

The advancement of modern manufacturing is synonymous with the growth of automation. Automation replaces human operators, improves productivity and quality, and reduces costs. However, the initial financial cost and knowledge requirements can be barriers to embracing automation. Manufacturers are now seeking smart manufacturing, known as the fourth industrial revolution. Smart manufacturing goes beyond automation and utilizes IoT, AI, and big data for optimized production. In a smart factory, production can be linked and controlled innovatively, leading to increased performance, agility, and reduced costs.

The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.

This essay explores how AI can enhance clinical research and, particularly, its pivotal role in the development of medical devices. A data-driven approach to medical device development that can streamline regulatory compliance and quality assurance is discussed. Methods that generate insights from pre-stage data and utilize it during development are detailed. The effectiveness of this approach in compliance audits, 510(k) submissions, and quality system audits - reducing time, effort, and risks is analyzed. The findings are illustrated with practical examples and takeaway recommendations. When reading a scientific article, how many times have you judged the quality of the research by looking at the methodology section? Artificial intelligence algorithms can be developed with the most robust and innovative technology, but if they are not properly validated, they will be worthless in the eyes of regulatory authorities. Conversely, outdated and simplistic models can still gain regulatory clearance if robustness is effectively demonstrated. For better or worse, ethics, economics, and robustness are often sacrificed in the constant government struggle to keep up with the technological edge of AI development. The slow crawl of lawmakers is constant in every field. Automating small tasks can save time and reduce risks when playing catch-up with a changing regulatory framework so the rest of the AI development can continue uninhibitedly. This dives into using FDA open data to collaborate with a food and drug law company and develop several bottom-up initiatives that supply knowledge needed for regulatory compliance and quality systems development. Methods that input pre-stage data and output actionable insights as models are provided. By sharing these resources and advice as academic researchers, efficiency in streamlining processes is maximized, thereby letting more time and resources be allocated to the actual development [1].

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, & Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, & Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, & Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.

This paper leverages gene and cell therapy research in diverse disorders ranging from monogenic to infectious diseases to cancer and emerging breakthroughs, where one can harness individual genes or a synthetic gene sequence designed based on a shared molecular pattern in infected cells to better fight various disorders [1]. A pivotal task is to predict the performances of candidate gene therapies to guide clinical translational research using methods such as retrospective bioinformatic analyses. Implementing them to a large-scale gene therapy database reveals that it is feasible to construct and apply well-performing interpretable, supervised learning models [2]. Preliminary evidence of machine learning approaches' statistical significance helps clinicians and biomedical researchers, market participants, and regulatory and economic experts derive relevant, practical applications, thereby enhancing the deployment of gene therapy and genomics to achieve positive, long-term growth for humanity while alleviating the ongoing worldwide economic burden precipitated by prolonged and recurring diseases. Deploying machine learning techniques to accelerate gene and cell therapy drug development and trials shall also mitigate the existing obstacle of limited patient access to emerging, transformative medical innovations such as gene therapy due to skyrocketing prices, which often herald gene therapy products as the world's most expensive medicines [3]. Moreover, in preventing patients from accessing effective, life-saving genetic medicines, there commonly exists a multidimensional access gap encompassing the availability, affordability, and quality or acceptability of these clinical treatments. The ensuing substantial gap has repeatedly been documented and mainly emanates from differential institutional and socio-political choices around resource allocation at international and domestic levels [4]. Particularly, it is also due to the stringent licensure and regulatory approval processes underpinned by insufficient evidence for novel safety and clinical efficacy profiles for genetic therapies in multiple micro-local diagnoses and subpopulations. We believe that a higher likelihood of gene therapy adoption shall result when the clinical evidence path contains adequate representation from the most diverse and relevant patient populations [5].