A man in his 60s with multiple vascular comorbidities presented with sudden, painless vision loss in one eye. Although he had a high risk for atherosclerotic events, initial evaluation for stroke was negative for acute ischemia, but found to have markedly elevated inflammatory markers. Accordingly, giant cell arteritis was investigated and Ophthalmologic findings and fulfillment of the 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria supported the diagnosis of giant cell arteritis, despite a negative temporal artery biopsy. Management included high-dose glucocorticoids and delayed tocilizumab initiation due to the need for multiple vascular surgeries. Vision loss was irreversible, but systemic symptoms resolved and vascular interventions were successful. This case highlights the diagnostic and management complexities of biopsy-negative giant cell arteritis in patients with severe atherosclerotic vascular disease, emphasizing the importance of clinical judgment and established classification criteria when imaging and biopsy results are inconclusive.

A residual-set framework is introduced for analyzing additive prime conjectures, with particular emphasis on the Strong Goldbach Conjecture (SGC). For each even integer $E_n\ge 4$, the residual set $ℛ\left(E_n\right)=\{E_n-p\mid$ $p<E_n,p\in \mathbb{P}\}$ is defined, and the universal residual set ${ℛ}_{\mathbb{E}}={{{\displaystyle \cup }}^{\text{}}}_{E_n}ℛ\left(E_n\right)$ is constructed. It is shown that ${ℛ}_{\mathbb{E}}$ contains infinitely many primes. A nontrivial constructive lower bound is derived, establishing that the number of Goldbach partitions satisfies $G\left(E\right)\ge 2$ for all $E\ge 8$, and that the cumulative partition count satisfies ${\displaystyle \sum }_{E\le N}G\left(E\right)\gg \frac{N^2}{{\text{log}}^{4}N}$. An optimized deterministic algorithm is implemented to verify the SGC for even integers up to 16,000 digits. Each computed partition $E_n=p+q$ is validated using elliptic curve primality testing, and no exceptions are observed. Runtime variability observed in the empirical tests corresponds with known fluctuations in prime density and modular residue distribution. A recursive construction is formulated for generating Goldbach partitions, using residual descent and leveraging properties of the residual sets. The method extends naturally to Lemoine's Conjecture, asserting that every odd integer $n\ge 7$ can be expressed as $n=p+2q$, where $p,q\in \mathbb{P}$. A corresponding residual formulation is developed, and it is proven that at least two valid partitions exist for all $n\ge 9$. Comparative analysis with the Hardy-Littlewood and Chen estimates is provided to contextualize the cumulative growth rate. The residual-set methodology offers a deterministic, scalable, and structurally grounded approach to additive problems in prime number theory, supported by both theoretical results and large-scale computational evidence.

Background: Proper umbilical cord care prevents neonatal infections and reduces neonatal mortality. Despite global recommendations for evidence-based cord care practices, traditional beliefs, and inadequate maternal knowledge often lead to unsafe practices, particularly in low-resource settings like Bangladesh. This study aimed to assess the understanding of umbilical cord care among mothers of neonates in Sherpur District, Bangladesh, and identify factors associated with knowledge levels. Methods: A descriptive cross-sectional study was conducted from July to October 2020 at Sherpur Sadar Hospital. A total of 193 mothers of neonates were recruited using a non-randomized purposive sampling method. Data was collected through a pre-tested, semi-structured, interviewer-administered questionnaire. Knowledge levels were categorized as "Good" (>6) or "Poor" (≤6) based on responses to 10 structured questions. Statistical analyses, including chi-square tests and crude odds ratios (COR), were performed to identify socio-demographic factors associated with knowledge levels. Results: Of the 193 participants, 48.7% demonstrated "Good" knowledge, while 51.3% had "Poor" knowledge. Education level (p = 0.01), occupation (p = 0.02), family type (p < 0.001), and family size (p = 0.04) were significantly associated with knowledge levels. Mothers with higher education and those from joint families exhibited better knowledge. However, 28.5% of respondents were unaware of the typical umbilical cord-shedding timeframe, and 44% could not identify signs of infection. Unsafe practices, such as using medications (14.5%) or hot compression (7.2%) for drying the cord, were reported. Conclusion: The study reveals significant gaps in maternal knowledge regarding umbilical cord care in Sherpur District, driven by socio-demographic disparities and cultural practices. Targeted health education programs, emphasizing evidence-based cord care practices and leveraging local social structures, are urgently needed to improve neonatal health outcomes in similar resource-limited settings. Future research should evaluate the effectiveness of these interventions to inform policy and practice.

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.

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.

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”.

The pulmonary characteristics of Staphylococcus aureus (S. aureus) co-infection with respiratory viruses, such as SARS-CoV-2 and influenza virus, are still unclear. Case series: Two patients with methicillin-susceptible S. aureus (MSSA) infection in the lungs co-infected with either SARS-CoV-2 or influenza virus are reported. Case 1 was a 66-year-old woman who was admitted with SARS-CoV-2 infection. Her chest X-ray and computed tomography (CT) showed multiple cavity formations with infiltration shadows, and MSSA was detected from her sputum and blood, suggesting COVID-19-related bacterial pneumonia and pulmonary embolism. No catheters had been used, but she had skin eruptions and a history of SARS-CoV-2 vaccination. Ampicillin/sulbactam (ABPC/SBT) was administered, and she finally improved. Case 2 was an 87-year-old man with a history of atopic dermatitis who was admitted with moderate pneumonia, and influenza virus co-infection was found. He showed multiple cavitary shadows, and MSSA was isolated from both his sputum and blood. He was diagnosed with influenza-related bacterial pulmonary embolism. No catheters had been used, but he had a history of influenza vaccination. He was also treated by ABPC/SBT and finally improved. Conclusions: These cases suggest that MSSA showed affinity to the lungs when co-infected with either SARS-CoV-2 or influenza virus, and it presented as septic emboli without catheter use. We should consider MSSA infection when patients have SARS-CoV-2 or influenza virus co-infection, and multiple cavity formation and skin disorders are seen, even though they were vaccinated and no catheters were used.

Background: There is paucity of data on health-related quality of life (HRQoL) among Health Information Managers/Health Record Officers (HROs) in the Nigeria health system. Hence, this study investigated the impact of the COVID-19 pandemic on health-related quality of life (QoL) among HROs in Obafemi Awolowo University Teaching Hospital Complex (OAUTHC), Ile-Ife, Nigeria. Methods: A cross-sectional study was conducted in the University Hospital, where a total of 52 health record officers were purposively sampled. Relevant data were collected using the Short Form survey (SF-36v2) questionnaire. One-way ANOVA was used to determine mean group differences across the nine and the two QoL (physical and mental) summary domains based on respondents’ socio-demographics, while level of significance was set at 0.05. Results: All the QoL sections of the instrument used yielded an α-Cronbach’s score of > 0.70. Analysis of some QoL physical component dimensions showed that; Bodily pain (BP) was found to be significantly (P=0.032) associated with marital status, Physical functioning (PF) with gender (P=0.023), and general health (GH) with age group (P=00.025) and highest level of education (P=0.023). On the other hand, mental health component analysis revealed that Social Functioning (SF) was associated with age group (P=014), Role limitation (RE) with marital status (P=0.048), highest level of education (P=0.048) and years of service (P=0.015) etc. Conclusion: The QoL among HROs studied was generally above average, and demographic characteristics such as age, gender and marital status significantly influence QoL. Health managers and stakeholders should consider some of the factors identified in managing HROs.

Background: The World Health Organization (WHO) has declared COVID-19 a public health emergency of international concern. In this context, effective and affordable diagnostic procedures are essential for identifying and managing cases. Complete blood counts (CBC) are among the most common and readily available diagnostic tests. The current study aimed to evaluate the efficacy of CBC in diagnosing COVID-19 and identifying cases. Patients and Methods: A case-control study was conducted on 173 patients at Ain Shams University Hospitals over a period of three months. Patients were allocated into two groups according to COVID-19 PCR results: Group 1 included patients with COVID-19 positive PCR, and Group 2 included patients with COVID-19 negative PCR. Results: The study found that differential CBC had significant value in diagnosing COVID-19 disease. Many COVID-19 patients had lymphopenia and leucopenia compared to non-COVID-19 suspected patients. The low values of leukocytes, neutrophils, lymphocytes, and eosinophils with a CBC test were found to be valuable in the initial diagnosis of COVID-19. Conclusion: The definitive diagnosis of COVID-19 requires RT-PCR analysis, which is time-consuming and less accessible. Thus, the initial diagnosis and treatment of patients may be delayed. This study suggests that CBC, which is easily available and affordable, can be valuable in the early identification of COVID-19 cases, allowing for prompt treatment and management.