Diversification of the VH3-53 immunoglobulin gene segment by somatic hypermutation results in neutralization of SARS-CoV-2 virus variants.

COVID-19 induces re-circulating long-lived memory B cells (MBC) that, upon re-encounter with the pathogen, are induced to mount immunoglobulin responses. During convalescence, antibodies are subjected to affinity maturation, which enhances the antibody binding strength and generates new specificities that neutralize virus variants. Here, we performed a single-cell RNA sequencing analysis of spike-specific B cells from a SARS-CoV-2 convalescent subject. After COVID-19 vaccination, matured infection-induced MBC underwent recall and differentiated into plasmablasts. Furthermore, the transcriptomic profiles of newly activated B cells transiently shifted toward the ones of atypical and CXCR3+ B cells and several B-cell clonotypes massively expanded. We expressed monoclonal antibodies (mAbs) from all B-cell clones from the largest clonotype that used the VH3-53 gene segment. The in vitro analysis revealed that some somatic hypermutations enhanced the neutralization breadth of mAbs in a putatively stochastic manner. Thus, somatic hypermutation of B-cell clonotypes generates an anticipatory memory that can neutralize new virus variants.

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications.

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 µg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 µg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Disaster after disaster: the outbreak of infectious diseases in Pakistan in the wake of 2022 floods.

In June 2022, Pakistan witnessed catastrophic floods, affecting millions of people. The ensuing epidemics of cholera, cryptosporidiosis, rotavirus infections, generalized diarrhoea, typhoid and paratyphoid fevers, as well as the frequency of vector-borne diseases including malaria and dengue fever, are studied in this investigation. It also explores the latest outbreak of poliomyelitis and the frequency of respiratory diseases such COVID-19, diphtheria, and tuberculosis, as well as how floods have contributed to skin and eye problems. The report also describes the obstacles governments must overcome in order to manage these health emergencies and offers possible solutions for reducing the effects of ongoing and anticipated epidemics. This flood emphasizes the pressing need for international action and acts as an alarming indicator of the significant impact of climate change. It emphasizes how crucial it is to have effective flood response and preparation strategies in developing nations that are vulnerable to natural disasters.

Linking green supply chain management practices and environmental performance in the manufacturing industry: a hybrid SEM-ANN approach.

This research determines the influence of green supply chain management practices (GSCM) on environmental performance. It also investigates the moderating role of supply chain environmental cooperation on GSCM practices and environmental performance relationships. A total of 370 employees of several Bangladeshi manufacturing companies were conveniently chosen as respondents. To verify the data validity and reliability and to test the hypotheses, we used SmartPLS. Finally, we employed an artificial neural network (ANN) to examine the relationship. Green design and green manufacturing have significant positive impacts on environmental performance, while green procurement and green distribution do not. Moreover, environmental cooperation moderates the relationships of green design and green distribution with environmental performance. The moderating effect of supply chain environmental cooperation in the relationship between GSCM practices and environmental performance in the manufacturing industry adds knowledge to the existing literature by incorporating a hybrid model combining PLS-SEM and ANN. Our study adds to the current body of knowledge by delving into the literature on GSCM from the perspective of Bangladesh's industrial sector. This study fills a knowledge gap by shedding light on the interactions of GSCM and environmental performance. Indeed, this study represents a step forward from classic linear regression-based models to an ANN-based nonlinear model. It also demonstrates new contributions to the literature on green supply chain management and environmental performance.

Explainable deep learning model for automatic mulberry leaf disease classification.

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world's raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for three-class classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models' findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDS-CNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves.

A reflection of Africa's cardiac surgery capacity to manage congenital heart defects: a perspective.

Congenital heart defects (CHDs) are birth abnormalities that may drastically alter the structure and functionality of the heart. For 70% of infants with congenital disorders to survive or maintain a better quality of life, surgery is necessary. Over 500 000 of the 1.5 million CHD cases reported annually, or 1% of all live births, occur in Africa, according to the WHO. A surmounted 90% of these patients are from Africa, and as a consequence, 300 000 infants die annually as a result of poor care or difficulty accessing adequate healthcare. However, the high prevalence of CHDs, precipitated by a plethora of aetiologies worldwide, is particularly pronounced in Africa due to maternal infectious diseases like syphilis and rubella amongst the pregnant populace. In low- and middle-income countries, especially in Africa, where foreign missions and organizations care for the majority of complicated cardiac surgical patients, access to secure and affordable cardiac surgical therapy is a substantial issue. Interventions for CHDs are very expensive in Africa as many of the continent's domiciles possess low expenditures and funding, thereby cannot afford the costs indicated by associated surgical treatments. Access to management and healthcare for CHDs is further hampered by a lack of trained surgical personnel, specialized tools, infrastructure, and diagnostic facilities in Africa.

A Case of Alcohol Withdrawal-Induced Central and Extrapontine Myelinolysis.

A 40-year-old female with a history of chronic alcohol use disorder presented with an acute intractable left-sided headache for three days and progressively worsening unsteady gait requiring a wheelchair to ambulate. The patient had a history of chronic alcoholism since 2019 but reported abstinence since September 2021. One month after quitting alcohol, she experienced a sudden deterioration in bilateral extremity neuropathy, forgetfulness, difficulty writing, and severe mood swings, which continued to worsen until her presentation in July 2022. Laboratory tests, including complete blood count and electrolyte levels, were within normal ranges. A previous MRI performed during the investigation for alcoholic neuropathy a few months before she quit drinking showed no abnormalities. However, a subsequent MRI during work-up for the current acute symptoms revealed significant signal abnormalities involving the central pons, bilateral cerebral peduncles, and medullary pyramids, consistent with chronic central pontine myelinolysis (CPM) with extrapontine myelinolysis (EPM) extending into the peduncles. The patient received treatment with folate and multivitamins and was scheduled for outpatient follow-up with physical therapy for rehabilitation. This case highlights CPM as a consequence of alcohol withdrawal and emphasizes the importance of timely diagnosis and appropriate management in such patients.

Recent Advancements in Epidural Etanercept for Pain Management in Radiculopathy: A Literature Review.

The most common etiology of low back and neck pain is associated with spinal cord pathologies. Regardless of origin, low back and neck pain are some of the most common causes of disability worldwide. Mechanical compression due to spinal cord diseases, such as degenerative disc disorders, can lead to radiculopathy, which manifests as numbness or tingling and can progress to loss of muscle function. Conservative management, such as physical therapy, has not been proven effective in treating radiculopathy, and surgical treatments have more risks than benefits for most patients. Epidural disease-modifying medications, such as Etanercept, have been recently explored due to their minimal invasiveness and direct effects on inhibiting tumor necrosis factor-α (TNF-α). Therefore, this literature review aims to evaluate epidural Etanercept's effect on radiculopathy caused by degenerative disc diseases. Epidural Etanercept has been shown to improve radiculopathy in patients with lumbar disc degeneration, spinal stenosis, and sciatica. Further research is needed to compare the effectiveness of Etanercept with commonly used treatments such as steroids and analgesia.

A Case of Acute Liver Failure Due to Artemisinin-Derived Herbal Supplements.

A 49-year-old female presented with malaise, nausea, vomiting, and discolored urine. She was found to have an acute liver failure with labs significant for aspartate aminotransferase (AST) of 2164, alanine aminotransferase (ALT) of 2425, alkaline phosphatase (ALP) of 106, total bilirubin of 3.6, and lactate dehydrogenase (LDH) of 2269. The international normalized ratio (INR) was also elevated at 1.9. All workup for acute liver failure was negative and it was found that she had started taking a new supplement called "Gut Health", which contained artemisinin, for weight loss and menopausal symptoms. After discontinuing the supplements and symptomatically treating her for acute liver failure, her transaminitis resolved.

The Potential Role of Etanercept in the Management of Post-stroke Pain: A Literature Review.

Strokes are the second leading cause of death and disability worldwide. The brain injury resulting from stroke produces a persistent neuroinflammatory response in the brain, resulting in a spectrum of neurologic dysfunction affecting stroke survivors chronically, also known as post-stroke pain. Excess production of tumor necrosis factor alpha (TNF alpha) in the cerebrospinal fluid (CSF) of stroke survivors has been implicated in post-stroke pain. Therefore, this literature review aims to assess and review the role of perispinal etanercept in the management of post-stroke pain. Several studies have shown statistically significant evidence that etanercept, a TNF alpha inhibitor, can reduce symptoms present in post-stroke syndrome by targeting the excess TNF alpha produced in the CSF. Studies have also shown improvements in not only post-stroke pain but also in traumatic brain injury and dementia. Further research is needed to explore the effects of TNF alpha on stroke prognosis and determine the optimal frequency and duration of etanercept treatment for post-stroke pain.

A biobased synthesized N-hydroxymethyl starch-amide for enhancing the wet and dry strength of paper products.

The aim of this research was to develop a bio-based paper strength agent for the replacement of petroleum-based paper strength agents. Cationic starch was modified with 2-chloroacetamide in aqueous media. The modification reaction conditions were optimized based on the acetamide functional group incorporated into cationic starch. Further, modified cationic starch was dissolved in water and then reacted with formaldehyde to produce N-hydroxymethyl starch-amide. 1 % N-hydroxymethyl starch-amide was mixed with OCC pulp slurry before preparing the paper sheet for testing the physical properties. The wet tensile index, dry tensile index, and dry burst index of the N-hydroxymethyl starch-amide-treated paper increased 243 %, 36 %, and 38 %, respectively, compared to the control sample. In addition, comparative studies were done between N-hydroxymethyl starch-amide and commercial paper wet strength agent GPAM and PAE. The wet tensile index of 1 % N-hydroxymethyl starch-amide-treated tissue paper was similar to GPAM and PAE, and 2.5 times higher than the control sample.

Sonographic Characteristics and Pathology Correlation of Breast Imaging Reporting and Data System (BI-RADS) Category 4 Lesions.

INTRODUCTION: The Breast Imaging-Reporting and Database System (BI-RADS) category 4 is designated for breast lumps that do not display the typical features of malignancy but still raise enough suspicion to warrant a recommendation for a biopsy, as malignancy cannot be ruled out through imaging alone. The main objective of this study was to investigate the sonographic characteristics and pathology correlation of BI-RADS 4 breast lesions and determine the positive predictive rate of BI-RADS 4 lesions in diagnosing breast cancer, using histopathology as the gold standard. METHODS: This was a cross-sectional study conducted at the Department of Radiology, Aga Khan University Hospital in Karachi, spanning from May 2021 to August 2022, with a duration of 15 months. The study focused on female patients over the age of 18 who presented with suspicious breast lesions on ultrasound. Both mammography and ultrasound-guided core needle biopsy were performed on these patients, followed by a detailed histopathological evaluation of the biopsy specimens. To calculate the positive predictive value (PPV), true positive cases were identified through both histopathology and ultrasonography. RESULTS: A total of 227 cases were categorized as BI-RADS 4 lesions, with the patients' mean age being 47.8 ± 14.3 years (range: 17 - 88). Among the biopsied lesions, 101 cases were confirmed to be true positive for breast malignancies, resulting in a PPV for malignancy of 44.9%. Conversely, there were 124 false positive cases out of the 227 BI-RADS 4 category lesions (54.63%). The primary indication for presentation was a breast lump, and out of the 101 confirmed malignant cases, 70 (69.3%) were associated with malignancy. CONCLUSION: BI-RADS 4 can be utilized to assess suspicious breast lumps; however, for more reliable results and to avoid false negatives, histopathological confirmation should complement the imaging findings.

Structural Homology-Based Drug Repurposing Approach for Targeting NSP12 SARS-CoV-2.

The severe acute respiratory syndrome coronavirus 2, also known as SARS-CoV-2, is the causative agent of the COVID-19 global pandemic. SARS-CoV-2 has a highly conserved non-structural protein 12 (NSP-12) involved in RNA-dependent RNA polymerase (RdRp) activity. For the identification of potential inhibitors for NSP-12, computational approaches such as the identification of homologous proteins that have been previously targeted by FDA-approved antivirals can be employed. Herein, homologous proteins of NSP-12 were retrieved from Protein DataBank (PDB) and the evolutionary conserved sequence and structure similarity of the active site of the RdRp domain of NSP-12 was characterized. The identified homologous structures of NSP-12 belonged to four viral families: Coronaviridae, Flaviviridae, Picornaviridae, and Caliciviridae, and shared evolutionary conserved relationships. The multiple sequences and structural alignment of homologous structures showed highly conserved amino acid residues that were located at the active site of the RdRp domain of NSP-12. The conserved active site of the RdRp domain of NSP-12 was evaluated for binding affinity with the FDA-approved antivirals, i.e., Sofosbuvir and Dasabuvir in a molecular docking study. The molecular docking of Sofosbuvir and Dasabuvir with the active site that contains conserved motifs (motif A-G) of the RdRp domain of NSP-12 revealed significant binding affinity. Furthermore, MD simulation also inferred the potency of Sofosbuvir and Dasabuvir. In conclusion, targeting the active site of the RdRp domain of NSP-12 with Dasabuvir and Sofosbuvir might reduce viral replication and pathogenicity and could be further studied for the treatment of SARS-CoV-2.

Role of Primary Caregivers Regarding Unintentional Injury Prevention Among Preschool Children: A Cross-Sectional Survey in Low- and Middle-Income Country.

Importance Unintentional childhood injuries significantly strain healthcare resources, and their preventable measures can significantly reduce morbidity and mortality. Objectives To investigate the role of primary caregivers in preventing unintentional injuries and to identify the groups that require special health intervention programs to reduce the burden of this public health concern. Methodology A cross-sectional survey was conducted at three hospitals in Karachi, Pakistan. Parents of preschool children who visited pediatric clinics were invited to participate in the study by completing a self-administered questionnaire comprising questions about knowledge, attitudes, and practices towards preventing unintentional injuries among children. Results With an 80% response rate, the overall mean knowledge, attitude, and practices (KAP) score was 27.40 ± 3.48. Only 14.3% of the participants had a high KAP score, while 83.6% and 2.1% of the respondents had moderate and low KAP scores, respectively. People of lower socioeconomic status, unemployed, less educated, and families with more than one preschool child were less knowledgeable and non-adherent to unintentional preventive injury. It was found that 21% of the children had suffered from an unintentional severe injury in the past, and the internet was the most frequent source of gaining knowledge among parents. Conclusion Parental knowledge, attitude, practices, and adherence to child safety measures are sub-optimal in our cohort of studied participants. Raising awareness and providing the counseling are essential in reducing the burden of unintentional injuries.

Plastic Burning Impacts on Atmospheric Fine Particulate Matter at Urban and Rural Sites in the USA and Bangladesh.

To better understand the impact of plastic burning on atmospheric fine particulate matter (PM2.5), we evaluated two methods for the quantification of 1,3,5-triphenylbenzene (TPB), a molecular tracer of plastic burning. Compared to traditional solvent-extraction gas chromatography mass spectrometry (GCMS) techniques, thermal-desorption (TD) GCMS provided higher throughput, lower limits of detection, more precise spike recoveries, a wider linear quantification range, and reduced solvent use. This method enabled quantification of TPB in fine particulate matter (PM2.5) samples collected at rural and urban sites in the USA and Bangladesh. These analyses demonstrated a measurable impact of plastic burning at 5 of the 6 study locations, with the largest absolute and relative TPB concentrations occurring in Dhaka, Bangladesh, where plastic burning is expected to be a significant source of PM2.5. Background-level contributions of plastic burning in the USA were estimated to be 0.004-0.03 µg m-3 of PM2.5 mass. Across the four sites in the USA, the lower estimate of plastic burning contributions to PM2.5 ranged 0.04-0.8%, while the median estimate ranged 0.3-3% (save for Atlanta, Georgia, in the wintertime at 2-7%). The results demonstrate a consistent presence of plastic burning emissions in ambient PM2.5 across urban and rural sites in the USA, with a relatively small impact in comparison to other anthropogenic combustion sources in most cases. Much higher TPB concentrations were observed in Dhaka, with estimated plastic burning impacts on PM2.5 ranging from a lower estimate of 0.3-1.8 µg m-3 (0.6-2% of PM2.5) and the median estimate ranging 2-35 µg m-3 (5-15% of PM2.5). The methodological advances and new measurements presented herein help to assess the air quality impacts of burning plastic more broadly.

Sources identification of ammonium in PM2.5 during monsoon season in Dhaka, Bangladesh.

Ammonia (NH3) is taken up by fine particulate matter (PM2.5), and there are concerns about its impact on the environment and health. The source of NH3, which was thought to be of agricultural sources, has recently been suspected to be non-agricultural sources in urban areas. Here, we collected PM2.5 during the monsoon season in Dhaka, Bangladesh, the most polluted city in the world, and analyzed the δ15N-NH4+ in PM2.5. As the result, the δ15N-NH4+ ranged from 9.2 ‰ to 34.4 ‰ (average: 20.7 ± 4.8 ‰), the highest of any of the averaged values annual reported in previous researches. In order to perform source analysis, the NH3 concentrations were estimated using the thermodynamic model ISORROPIA-II. The estimated concentration of NH3 gas averaged 40.8 µg/m3 (3.0-154.6 µg/m3). The contributions calculated with the mixing model to the δ15N-NH4+ values in PM2.5 in Dhaka, Bangladesh averaged 25.3 ± 14 %, 22.8 ± 10 %, 26.5 ± 15 %, and 25.4 ± 10 % for waste, fertilizer, NH3 slip, and fossil fuel combustion, respectively. Non-agricultural sources (NH3 slip, and fossil fuel combustion) accounted for almost half (51.9 %) of the contributions. In addition, the several validation tests of the isotope mixing model were also performed. For validating the uncorrected and corrected source data for δ15N-NH3, the contribution of non-agricultural sources with uncorrected source data would have been very high (>80 %), much higher than the corrected source data.

In-kitchen aerosol exposure in twelve cities across the globe.

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM2.5 and PM10) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with âˆ¼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) concentrations were in Dhaka 185 ± 48 (220 ± 58) µg m-3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) µg m-3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM2.5 and PM10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m3; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions.