Objective: To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. Methodology: A structured review was conducted following PRISMA 2020 guidelines. Thirty-one peer-reviewed studies were selected through a targeted manual search based on predefined eligibility criteria. Included studies evaluated functional recovery following TKA using validated outcome measures such as the WOMAC, KSS, KOOS, IKDC, SF-36, and SANE. Data extraction focused on the instruments used, patient population characteristics, and reported outcomes. A descriptive synthesis was compiled in Table 1. Additionally, 15 studies with quantitative data were analyzed using a forest plot to illustrate risk ratios (RR) and 95% confidence intervals (CI) for functional improvement. Risk of bias was assessed qualitatively based on methodological rigor, clarity of reporting, and validation of the outcome tools. Results: All included studies reported improvements in functional status following TKA. Most risk ratios ranged from 0.66 to 0.85, indicating a consistent reduction in the risk of postoperative functional limitation. High-quality studies demonstrated more precise effect estimates and greater internal validity. The SANE scale emerged as a valid and practical tool with high responsiveness, including in its culturally adapted Brazilian version. Despite heterogeneity in study design, the direction of effect remained consistent across all included studies. Conclusion: Validated functional assessment tools are essential for monitoring recovery after total knee arthroplasty. Instruments such as WOMAC and SANE demonstrate strong clinical utility and psychometric validity. Their systematic use enhances outcome comparability, supports individualized rehabilitation planning, and improves decision-making in orthopedic care.

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.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Background: The study of phenolic compounds and their potential to contribute to health is a major interest in research. This work was to determine phenolic compound contents as well as antioxidant properties of roasted maize-peanut snack product with and without spices. Methods: HPLC was used to determine the phenolic composition of the maize flours, peanut flour and their composite snacks with and without spices. Total phenolic content (TPC), total flavonoid content (TFC), tannin content (TC) and radical scavenging activity (measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-azino-bis (3- ethylbenzothiazoline-6-sulphonicacid) (ABTS) and hydrogen peroxide radical scavenging assays was also used. Results: TPC of the extract of roasted maize flour, roasted peanut flour and composite roasted maize-peanut flour ranged from 48.93 to 178.31 mg GAE/100 g, while the TFC was 3.18–25.94 mg CE/100 g and TC (0.22 – 0.73 mg CE/g). The dominant phenolic acid was protocatechuic acid ranged from 13.73 to 1643.54 µg/g. Among the flavonoids, quercetin and catechin were dominant. The extracts of the free soluble fraction exhibited 23.88 – 81.52 %, 49.59 – 85.17 % and 0.58 -5.13 µmol AAE/g of DPPH, hydrogen peroxide and ABTS radical scavenging abilities respectively. Conclusion: Maize–peanut product showed potential ability in contributing to alleviating radical induced oxidative stress.

This work evaluated the bioremediation potential of Jatropha tanjorensis leaf extract at different masses (250g, 500g and 750g) over a 40-day period. To achieve this, crude oil contamination of sandy loam soil was stimulated in twelve plastic reactors containing fixed masses of soil (4kg each) of topsoil homogenized with 500g of Bonny light crude oil. The Jatropha tanjorensis leaves were cultivated, rinsed with distilled water, blended, and purified by filtration. The leaf extract was applied at the stated concentrations including a control reactor (without leaf extract). The plastics reactors were kept in an open air shielded away from rainfall. The physicochemical characteristics determined were particle size distribution (PSD), potential of hydrogen (pH), electrical conductivity (EC), organic matter (OM), organic carbon (OC), selected heavy metals (Cr, Cd, Zn, Pb) and sample management were all in line with standard procedure. After 40 days of treatment, results obtained showed that plastic reactor with 750g of leaf extract produced the highest amount of cadmium reduction of 97% (from an initial of and there was significant difference among treatment (P < 0.05). The sequence of reduction among treatment was 750g > 500g > 250g of the leaf extract. Chromium, Lead and zinc followed similar trend. Thus, the Jatropha tanjorensis leaf extract has the potential to ameliorate crude oil-contaminated soil.

The price of grains like maize and sorghum is subject to significant fluctuations, which can have a significant impact on a country's economy and food security. The aim of the study is to model sorghum and maize price volatility in Nigeria. The data utilized in the study was extracted from World Bank Commodity Price Data (WBCPD), 2022. The data consists of monthly prices in nominal US dollars for maize and sorghum from January 1960 – August 2022. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were utilized for capturing the two-grain price volatility. Two types of conditional heteroscedastic models exist, the first group uses exact functions to control the evolution of , while the second group describes with stochastic equations. It is inferred from the result that inherent uncertainties and fluctuations existed in the prices of maize and sorghum in Nigeria which implies that the price volatility is positive and statistically significant suggesting that historical information and past shocks play a crucial role in determining the volatility observed in the grains. It is recommended that the ARCH, GARCH, EGARCH, TGARCH, PARCH, CGARCH, and IGARCH models should be employed for modeling and managing the volatility of maize and sorghum prices in Nigeria. These models have shown effectiveness in capturing different aspects of volatility, including the impact of past shocks, conditional volatility, asymmetry, and other relevant factors.

The accurate estimation of Individual Wave Components (IWC) is crucial for automated diagnosis of the human digestive system in a clinical setting. However, this process can be challenging due to signal contamination by other signal sources in the body, such as the lungs and heart, as well as environmental noise. To address this issue, various denoising techniques are commonly employed in bowel sound signal processing. While denoising is important, it can increase computational complexity, making it challenging for portable devices. Therefore, signal processing algorithms often require a trade-off between fidelity and computational complexity. This study aims to evaluate an IWC parameter extraction algorithm that was previously developed and reconstruct the IWC without denoising using synthetic and clinical data. To that end, the role of a reliable model in creating synthetic data is paramount. The rigorous testing of the algorithm is limited by the availability of quality and quantity recorded data. To overcome this challenge, a mathematical model has been proposed to generate synthetic bowel sound data that can be used to test new algorithms. The proposed algorithm’s robust performance is evaluated using both synthetic and clinically recorded data. We perform time-frequency analysis of original and reconstructed bowel sound signals in various digestive system states and characterize the performance using Monte Carlo simulation when denoising is not applied. Overall, our study presents a promising algorithm for accurate IWC estimation that can be useful for predicting anomalies in the digestive system.

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.

Today, the presentation of a three-dimensional model of real-world features is very important and widely used and has attracted the attention of researchers in various fields, including surveying and spatial information systems, and those interested in the three-dimensional reconstruction of buildings. The building is the key part of the information in a three-dimensional city model, so extracting and modeling buildings from remote sensing data is an important step in building a digital model of a city. LiDAR technology due to its ability to map in all three modes of one-dimensional, two-dimensional, and three-dimensional is a suitable solution to provide hyperspectral and comprehensive images of the building in an urban environment. In this review article, a comprehensive review of the methods used in identifying buildings from the past to the present and appropriate solutions for the future is discussed.

Novel corona virus disease (COVID-19) pandemic, which started in China's Hubei province in 2019, has caused a significant loss of human lives globally. This study describes the epidemiologic and clinical profiles of COVID-19 related deaths among patients admitted to treatment centers in Sidama region, Ethiopia. A cross-sectional study of 186 in hospital COVID-19 related deaths that occurred from July 2020 to December 2021 in Sidama region were analyzed. Data was extracted from regional emergency operation center death report. Data was entered using Epidata v3.1 and analysis was done using SPSS v.20. Categorical data was summarized using frequency and percentage while continuous data was summarized using median and interquartile range. Association between variables was assessed using chi-square test. More than two-third of the deceased patients were male (135; 72.6%) and median age at death was 60. The majority of deaths (151; 81.1%) occurred in 2021, while April 2021 had the highest death records. Cough and shortness of breath were the main presenting symptoms occurring in 89.2% and 85.5% of deceased patients respectively. Most of the COVID-19 related deaths (64.5%) had associated comorbidities. Diabetes (50%) and Hypertension (39.2%) were the most prevalent comorbidities. Significant proportion of patients (74.73%) presented on severe end of disease spectrum (critical/ severe). Of the deceased patients, around two-third required Intensive care unit (ICU) admission and 111 of them were put on mechanical ventilator. Moreover, the median ICU stay was 4 days. Around half of the death (48.4%) occurred in the first 5 days. The median survival time from symptom onset was 11.5 days with most (43.5%) of the deaths occurring within the first 14 days of symptom onset. Age category was significantly associated with the number of days from onset to death (p=0.006). The case fatality rate was 1.87% which is lower than national and global reports. Unlike previous studies, the prevalence of asthma among deceased patients was low and there were no patients with documented COPD.

During the past few decades, many of the synthetic chemicals are able to produce nanoparticles and nanoclusters, although these chemicals primarily act as reducing and capping agents, they are very toxic and hazardous and make the nanoparticles biologically incompatible. Thus there is need for green chemistry that includes a clean, non-toxic and environmental friendly method of nanoparticles synthesis. Cobalt, iron and copper nanoparticles were synthesized using the stem-bark extract of khayasenegalensis (mahogany) where cobalt chloride (CoCl₂ 6H₂O), ferric chloride (FeCl₂), and copper sulphate (CuSO₄ H₂O) were used as the metal precursor respectively. The change in color from light brown to dark brown indicates the formation of cobalt nanoparticles, from light brown to dark green indicates the formation of copper nanoparticles and also the change in color from light brown to a dark color indicates formation of iron nanoparticles. The nanoparticles were further characterized using UV visible spectroscopy, FTIR, and SEM. The UV result for CoNPs showed the highest peak at 500nm and both FeNPs and CuNPs showed the highest peak at 300nm. The FTIR results for all the nanoparticles showed the presence of Alkaloids and triterpenes. Also the SEM result showed spherical granular, partially dispersed and monodispersed morphology for CoNPs, FeNPs and CuNPs respectively. Moreover, the antibacterial activity of the synthesized NPs when tested against two gram positive bacteria and two gram negative bacteria was evaluated and good results were obtained. The antifungal activity when tested against two fungi showed a very good result.

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.

Background: Although some neural mechanisms underlying socioeconomic status (SES) disparities are known, the role of brain-wide resting-state functional connectivity in these effects remains less understood. Aim: This study aims to identify brain-wide resting-state functional connectivity signatures that may mediate the effects of SES on body mass index (BMI) and blood pressure in children, using data from the Adolescent Brain Cognitive Development (ABCD) study. Methods: Data were drawn from the ABCD study, a large, diverse cohort of children aged 9-10. Pre-processed resting-state functional MRI data were used, and factor analysis was conducted to extract a whole-brain connectivity factor. The first factor, capturing the greatest variance in brain-wide resting-state connectivity, was selected for further analysis in a structural equation model (SEM). This connectivity factor was tested as a potential mediator of the relationship between SES (measured by parental education, family income, and neighborhood characteristics) and two indicators of cardiometabolic health: BMI and systolic blood pressure. Results: Factor analysis revealed a robust first factor that accounted for a significant proportion of variance in brain-wide resting-state functional connectivity. This factor was significantly associated with SES, indicating that children from lower SES backgrounds exhibited distinct connectivity patterns. Additionally, the factor was linked to both BMI and systolic blood pressure, suggesting its relevance to cardiometabolic health. Mediation analysis showed that this connectivity factor partially mediated the relationship between SES and both BMI and systolic blood pressure. Conclusions: Brain-wide functional connectivity may be a mediator of SES effects on BMI and blood pressure in children. The first connectivity factor provides a promising neural signature linking SES with cardiometabolic risk. Comprehensive brain-wide approaches to functional connectivity may offer valuable insights into how social determinants of health shape neural and physical development in childhood.

Groundwater is undeniably crucial to people's lives, particularly in coastal regions. Therefore, it is imperative to address this vital water source strategically and implement a management plan to maintain its optimal state. The salinization of groundwater poses a significant challenge for coastal communities, stemming from factors like excessive groundwater extraction from coastal aquifers, reduced recharge, rising sea levels, climate change, and other causes. Saltwater intrusion (SWI) is a prevalent issue that needs attention, as it significantly threatens groundwater quantity and quality. SWI happens when saline water infiltrates coastal aquifers, contaminating freshwater supplies. This review article aims to define SWI, explore its causes and influencing factors, and discuss various monitoring techniques. Additionally, it examines different modeling methods and management tools, including remote sensing, field surveys, modeling approaches, and optimization techniques. To mitigate the adverse effects of SWI, several control measures are outlined, along with their pros and cons. The final section reviews previous SWI studies and case studies from the Nile Delta, Sinai Peninsula, and North-West coast in Egypt. These studies offer suggestions, adaptations, and mitigation measures for future research.

Business merchants and investors in Nigeria are interested in the foreign exchange volatility forecasting accuracy performance because they need information on how volatile the exchange rate will be in the future. In the paper, we compared Exponential Generalized Autoregressive Conditional Heteroskedasticity with order p=1 and q= 1, (EGARCH (1,1)) and Recurrent Neural Network (RNN) based on long short term memory (LSTM) model with the combinations of p = 10 and q = 1 layers to model the volatility of Nigerian exchange rates. Our goal is to determine the preferred model for predicting Nigeria’s Naira exchange rate volatility with Euro, Pounds and US Dollars. The dataset of monthly exchange rates of the Nigerian Naira to US dollar, Euro and Pound Sterling for the period December 2001 – August 2023 was extracted from the Central Bank of Nigeria Statistical Bulletin. The model efficiency and performance was measured with the Mean Squared Error (MSE) criteria. The results indicated that the Nigeria exchange rate volatility is asymmetric, and leverage effects are evident in the results of the EGARCH (1, 1) model. It was observed also that there is a steady increase in the Nigeria Naira exchange rate with the euro, pounds sterling and US dollar from 2016 to its highest peak in 2023. Result of the comparative analysis indicated that, EGARCH (1,1) performed better than the LSTM model because it provided a smaller MSE values of 224.7, 231.3 and 138.5 for euros, pounds sterling and US Dollars respectively.

Calotropis procera, a medicinally important plant found in Asia, was explored for its anticancer and antibacterial properties in this study. The leaves of C. procera were extracted using methanol and FTIR and UV-VIS spectrophotometry were used to characterize them. Using the MTT assay and the disc diffusion test, the extract was examined for anticancer activity against the MCF7 breast cancer cell line and antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). The methanolic fraction of C. procera was found to be efficient against the MCF7 cell line and dramatically suppressed MRSA growth. The metabolic fraction of C. procera leaves is important in suppressing the growth of the MCF7 cell line, and it has the potential to be an effective antibacterial agent, according to our findings. The implications of Calotropis procera for all healthcare professionals including oncologists, physicians, pharmacists, nurses, and nutritional therapists are significant. With the increasing incidence of cancer and antibiotic-resistant bacterial infections, there is a growing need for new, effective, and safer herbal treatments.

Oncology nurses are more likely to get compassion fatigue (CF) than nurses in other fields because of the emotional stress and poor outlook of cancer patients. Because of this, the care might not be very good, the job might not be very satisfying, and there is a good chance that the patient's pain won't be noticed. Aim. To synthesize empirical evidence on compassion fatigue in order to extract the common, central, and fundamental elements that may improve nursing care. Design. An integrative review Results. Fifteen (15) studies met the eligibility criteria wherein five themes emerged. These are the level of compassion fatigue among oncology nurses, the oncology nurses' perspectives on compassion fatigue, precipitating factors leading to CF with 2 subthemes (work environment and a feeling of lack of support), the influence of compassion fatigue on the personal lives and general well-being of cancer nurses, and the consequences on the quality of oncology nurses' professional lives at work. Conclusion. CF is a significant problem for nurses who work in specialized areas such as cancer units, demonstrated as a basic incapacity to nurture others. The integration of studies provides evidence of clinical practice application which can provide better outcomes and improve nursing care. Implications for Practice. The findings provide understanding into healthcare practice on how to avoid compassion fatigue. Clinical management approaches that can mitigate compassion fatigue and its negative repercussions are presented, as well as the formation of peer support groups that have the ability to ameliorate CF.

Effective Inclusive education experiences can be built through structured interventions. The purpose of the study was to assess the impact of inclusive education experiences on teacher-trainees with sensory impairment in the Ghana Colleges of Educations of Ghana. The study was based on pragmatist philosophy. The study adopted convergent parallel mixed-methods approach. The population involved all 66 students with sensory (visual and hearing) impaired in the three (3) CoEs (PCE, Akropong Akwapim, WESCO, Kumasi and NJA, Wa) that practice inclusive education (IE) during the 2018/19 academic year. Purposive and census sampling techniques were used to select the three (3) colleges of education and sixty-six students for the study. The main instruments for data collection were questionnaire and focus group discussion. The quantitative data items were coded for input into the Statistical Product and Service Solutions (SPSS) version 23 software and analysed using means and standard deviations. The qualitative extracts collected into themes that were coded, analysed and interpreted. The study revealed that teacher-trainees had varied experiences on campus, while they felt welcomed into the inclusive institution; they also felt the Colleges were not well prepared to meet their needs. The physical environment was not conducive for the VI on campus. It is recommended that, College authorities should work with the MoE and agencies concerned with disability issues in the society to provide comfortable environment on College campuses for TTSI. It is also recommended that, providing a comfortable environment should include facilities and resources needed for the TTSI to learn effectively. It also involves physical arrangement of the campus environment. The TTSI, regardless of their disabilities, should be provided with an environment where their movement, their studies, their interactions with their peers and tutors are made easier to help them graduate successfully.

Although human oral fluid has become more routine for quantitative drug detection in pain management, detecting a large scope of medications and substances is costly and technically challenging for laboratories. This paper presents a quantitative assay for 64 pain medications, illicit substances, and drug metabolites in human oral fluid. The novelty of this assay is that it was developed on an older model AB SCIEX 4000 instrument and renders obscure the need for more technical and expensive laboratory equipment. This method includes addition of internal standard and a 2-step liquid-liquid extraction and dry-down step to concentrate and clean the samples. The samples were suspended in 50% MeOH in water and separation and detection was accomplished using triple quadrupole mass spectrometry (LC-MS/MS). Separation was achieved using reverse-phase liquid chromatography with detection by LC-MS/MS. A second injection was done in negative mode to determine THC-COOH concentration as an indicator of THC. An aliquot of the (already) extracted samples was analyzed for D- and L- isomers of amphetamine and methamphetamine using a chiral column. The standard curve spanned from 5 to 2000 ng/mL for most of the analytes (1 to 2000 ng/mL for fentanyl and THC-COOH) and up to 1000 ng/mL for 13 analytes. Pregabalin and gabapentin ranged from 25 to 2000 ng/mL. The result is a low-cost method for the sensitive detection of a wide-ranging oral fluid menu for pain management. This assay has a high sensitivity, and good precision and accuracy for all analytes with an older model mass spectrometer.