Targeting cancer using scaffold-hopping approaches: illuminating SAR to improve drug design.

Scaffold hopping is a design approach involving alterations to the core structure of an already bioactive scaffold to generate novel molecules to discover bioactive hit compounds with innovative core structures. Scaffold hopping enhances selectivity and potency while maintaining physicochemical, pharmacodynamic (PD), and pharmacokinetic (PK) properties, including toxicity parameters. Numerous molecules have been designed based on a scaffold-hopping strategy that showed potent inhibition activity against multiple targets for the diverse types of malignancy. In this review, we critically discuss recent applications of scaffold hopping along with essential components of medicinal chemistry, such as structure-activity relationship (SAR) profiles. Moreover, we shed light on the limitations and challenges associated with scaffold hopping-based anticancer drug discovery.

Deep learning-driven dyslexia detection model using multi-modality data.

Background: Dyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. Methods: In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results: The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

Bone effects of metformin monotherapy and its combination with teneligliptin: A 12-week follow-up study in patients with type 2 diabetes mellitus.

AIMS: The skeletal effects of metformin monotherapy and in combination with teneligliptin are not well illustrated in patients with T2DM. To address this, we conducted an observational study to evaluate the effect of these oral hypoglycemic agents on bone turnover markers. METHODS: We recruited patients with T2DM and first-ever prescribed metformin monotherapy or metformin combined with teneligliptin from a tertiary care teaching hospital in New Delhi, North India. Both bone formation and resorption markers, IL-6 and PTD, were estimated along with glycated hemoglobin at baseline and 12 weeks. RESULTS: In both groups, hbA1c levels decreased significantly from baseline to 12 weeks. In the metformin-treated group, ß-CTX, sRANKL, IL-6, and PTD decreased significantly, and no significant changes were observed in P1NP, OC, BAP, or OPG at 12 weeks from baseline. In the metformin + teneligliptin group, BAP, ß-CTX, sRANKL, IL-6, and PTD decreased significantly, and no significant changes were observed in P1NP, OC, or OPG after 12 weeks from baseline. CONCLUSIONS: The positive bone outcome of metformin or teneligliptin was linked to bone resorption rather than bone formation and was independent of changes in HbA1c or PTD. However, these results must be confirmed with well-designed RCTs with more extended follow-up periods.

A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.

The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study's objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual's conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.

1,2,4,5-Tetraoxane derivatives/hybrids as potent antimalarial endoperoxides: Chronological advancements, structure-activity relationship (SAR) studies and future perspectives.

Malaria is a life-threatening disease that affects tropical and subtropical regions worldwide. Various drugs were used to treat malaria, including artemisinin and derivatives, antibiotics (tetracycline, doxycycline), quinolines (chloroquine, amodiaquine), and folate antagonists (sulfadoxine and pyrimethamine). Since the malarial parasites developed drug resistance, there is a need to develop new chemical entities with high efficacy and low toxicity. In this context, 1,2,4,5-tetraoxanes emerged as an essential scaffold and have shown promising antimalarial activity. To improve activity and overcome resistance to various antimalarial drugs; 1,2,4,5-tetraoxanes were fused with various aryl/heteroaryl/alicyclic/spiro moieties (steroid-based 1,2,4,5-tetraoxanes, triazine-based 1,2,4,5-tetraoxanes, aminoquinoline-based 1,2,4,5-tetraoxanes, dispiro-based 1,2,4,5-tetraoxanes, piperidine-based 1,2,4,5-tetraoxanes and diaryl-based 1,2,4,5-tetraoxanes). The present review aims to focus on covering the relevant literature published during the past 30 years (1992-2022). We summarize the most significant in vitro, in vivo results and structure-activity relationship studies of 1,2,4,5-tetraoxane-based hybrids as antimalarial agents. The structural evolution of different hybrids can provide the framework for the future development of 1,2,4,5-tetraoxane-based hybrids to treat malaria.

A pilot study on the "ScoliBrace" in the treatment of adolescent idiopathic scoliosis.

PURPOSE: Bracing for adolescents with idiopathic scoliosis (AIS) is a treatment option to prevent curve progression to surgical level. This study aimed to assess the efficacy of a 3D fully customized over corrective brace, "ScoliBrace," an orthosis treatment for AIS. METHODS: This was a prospective pilot study of AIS female patients with inclusion criteria followed recommended Scoliosis Research Society (SRS) Guidelines. Cobb angles measured at: baseline (T0), 21 months (T5-2), skeletal maturity (T6), 6 months post-brace (T7), along with hours of brace wear using a thermal sensor and health-related quality of life (HRQoL) using the SRS-22r questionnaire. RESULTS: A total of 30 female AIS patients with mean age 11.85 ± 0.68 years, predominantly Risser 0 (70%), and median Cobb angle 29° were recruited; 21 patients were included for the final analysis. Results showed significant difference in Cobb angle between T0 and T5-2 (median = 22.5° vs. 28.5°, p = 0.0082). 57.14% had reduction in Cobb angle by ≥ 5° at skeletal maturity. Cobb angle reduced 0.794° for each additional hour of dosage (p = 0.036, 95% CI = - 1.532°, - 0.056°). Although pain level was increased at T6 (4.37 ± 0.51vs.4.70 ± 0.41, p = 0.014), patients reported significantly greater satisfaction with management of their condition (3.90 ± 0.90vs.3.29 ± 0.88, p = 0.020). CONCLUSION: Results show similar findings to the BRAIST study, whereby curves remained under surgical threshold and showed improvement. More than half had curve reduction of ≥ 5° at skeletal maturity. Increased dose was also associated with improved outcomes. Using "ScoliBrace" as a non-surgical treatment, maintained curves below surgical threshold and showed curve reduction, improving patient satisfaction with management.

Inflammatory endobronchial polyps unleashing recurrent pneumothorax: A case report.

Inflammatory endobronchial polyps (IEPs) are rare benign lesions that originate from the bronchial mucosa. While pneumothorax is a well-known complication of various pulmonary conditions, its association with IEPs is exceedingly uncommon and poorly understood. This case report presents a unique and explosive encounter of a patient with an inflammatory endobronchial polyp who experienced a pneumothorax, shedding light on the clinical presentation, diagnostic challenges, and management strategies for this rare entity.

An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images.

Breast cancer (BC) is the leading cause of mortality among women across the world. Earlier screening of BC can significantly reduce the mortality rate and assist the diagnostic process to increase the survival rate. Researchers employ deep learning (DL) techniques to detect BC using mammogram images. However, these techniques are resource-intensive, leading to implementation complexities in real-life environments. The performance of convolutional neural network (CNN) models depends on the quality of mammogram images. Thus, this study aimed to build a model to detect BC using a DL technique. Image preprocessing techniques were used to enhance image quality. The authors developed a CNN model using the EfficientNet B7 model's weights to extract the image features. Multi-class classification of BC images was performed using the LightGBM model. The Optuna algorithm was used to fine-tune LightGBM for image classification. In addition, a quantization-aware training (QAT) strategy was followed to implement the proposed model in a resource-constrained environment. The authors generalized the proposed model using the CBIS-DDSM and CMMD datasets. Additionally, they combined these two datasets to ensure the model's generalizability to diverse images. The experimental findings revealed that the suggested BC detection model produced a promising result. The proposed BC detection model obtained an accuracy of 99.4%, 99.9%, and 97.0%, and Kappa (K) values of 96.9%, 96.9%, and 94.1% in the CBIS-DDSM, CMMD, and combined datasets. The recommended model streamlined the BC detection process in order to achieve an exceptional outcome. It can be deployed in a real-life environment to support physicians in making effective decisions. Graph convolutional networks can be used to improve the performance of the proposed model.

The impact of antiseizure medication on bone heath: A systematic review of animal studies.

BACKGROUND: Antiseizure medications (ASMs) are known to potentially impact bone health, but existing literature presents conflicting results regarding their specific effects on bone mineralization, metabolism, and quality. OBJECTIVE: This systematic review aims to establish a consensus regarding the influence of ASMs on bone health based on existing preclinical studies. METHODS: Following SYRCLE and PRISMA guidelines, we conducted a systematic search in PubMed, Science Direct, and Google Scholar to identify relevant studies. Ultimately, 21 articles were selected for inclusion in this review. RESULTS: Among the chosen studies, approximately half involved Wistar rats as experimental subjects. Levetiracetam and sodium valproate were the most frequently investigated drugs, with a typical treatment duration of 10-12 weeks. These studies exhibited a low risk of bias in aspects like sequence generation, random housing, random outcome assessment, and reporting bias. However, blinding in performance, allocation concealment, and detection were often rated as having a high risk of bias. The collective findings suggest that prolonged ASM use leads to reduced bone mineral density, altered bone turnover marker levels (including hypovitaminosis D, hypocalcemia, and secondary hyperparathyroidism), deterioration of bone microarchitecture, and decreased mechanical strength. CONCLUSION: The adverse effects on bone associated with ASMs are not limited to enzyme-inducing drugs, as newer generation ASMs may also contribute to these effects. Hypovitaminosis D alone may not be solely responsible for ASM-induced bone issues, suggesting the involvement of other mechanisms. Furthermore, substantial variations were observed in the results of different preclinical studies on individual ASMs, highlighting the need to standardize animal study methodologies to enhance reproducibility and reduce variation.

A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique.

Diabetic retinopathy (DR) is a severe complication of diabetes. It affects a large portion of the population of the Kingdom of Saudi Arabia. Existing systems assist clinicians in treating DR patients. However, these systems entail significantly high computational costs. In addition, dataset imbalances may lead existing DR detection systems to produce false positive outcomes. Therefore, the author intended to develop a lightweight deep-learning (DL)-based DR-severity grading system that could be used with limited computational resources. The proposed model followed an image pre-processing approach to overcome the noise and artifacts found in fundus images. A feature extraction process using the You Only Look Once (Yolo) V7 technique was suggested. It was used to provide feature sets. The author employed a tailored quantum marine predator algorithm (QMPA) for selecting appropriate features. A hyperparameter-optimized MobileNet V3 model was utilized for predicting severity levels using images. The author generalized the proposed model using the APTOS and EyePacs datasets. The APTOS dataset contained 5590 fundus images, whereas the EyePacs dataset included 35,100 images. The outcome of the comparative analysis revealed that the proposed model achieved an accuracy of 98.0 and 98.4 and an F1 Score of 93.7 and 93.1 in the APTOS and EyePacs datasets, respectively. In terms of computational complexity, the proposed DR model required fewer parameters, fewer floating-point operations (FLOPs), a lower learning rate, and less training time to learn the key patterns of the fundus images. The lightweight nature of the proposed model can allow healthcare centers to serve patients in remote locations. The proposed model can be implemented as a mobile application to support clinicians in treating DR patients. In the future, the author will focus on improving the proposed model's efficiency to detect DR from low-quality fundus images.

Infective Endocarditis Complicated by Iatrogenic Intracranial Hemorrhage Secondary to Cefazolin-Induced Coagulopathy.

Infective endocarditis can be acute or subacute. It can be caused by viral, bacterial, fungal, and sometimes nonbacterial etiologies. It is an important cause of mortality and morbidity in children as well as adolescents, despite advances in management. A 59-year-old male with a past medical history of aortic valve (AV) replacement on warfarin presented to the Emergency Department with dull right flank pain and poor dentition on examination. Computerized tomography (CT) scans of the abdomen revealed the presence of splenic and renal infarcts. Warfarin was held after the international normalized ratio (INR) was noted to be elevated at 11. Following the activation of the sepsis bundle in the ER, he received intravenous fluids (30 cc/kg) and was started on vancomycin and ceftriaxone. On further evaluation, the transesophageal echocardiogram revealed mobile densities on the aortic surface concerning vegetation. Antibiotics were transitioned to cefazolin, gentamycin, and rifampin for the management of prosthetic valve endocarditis. The patient's INR improved to 3.5 on the third day of hospitalization, and heparin was initiated to maintain anticoagulation for the prosthetic valve. However, on the eighth day of hospitalization, the patient developed left-sided weakness and slurred speech. The CT head showed acute frontoparietal intracranial hemorrhage (ICH), with an INR noted to be 5. Heparin was reversed with protamine sulfate, and vitamin K was administered, following which the INR improved to 2.3. The patient was transferred to intensive care, but on the second day of the ICU stay, the INR again shot up to 6 with normal LFTS. The patient received vitamin K, but the INR only improved to 5. Subsequently, antibiotics were changed from cefazolin to nafcillin. INR thus fell to 1.6 in two days after changing the antibiotics. The patient was soon transferred to a higher center for aortic valve replacement. While few case reports have described severe coagulopathy induced by cefazolin, it is particularly seen with impaired renal function; however, our patient's renal function was completely normal. Coagulopathy is due to the drug's effect on intestinal flora and its structural methyl-thiadiazole side chain, which has similar effects as epoxide reductase inhibitors and results in INR elevation. Patients on cefazolin need to be closely monitored for INR levels every day, as there is a high likelihood of developing complications like ICH, as noted in this patient. While the monitoring of cefazolin levels is not necessarily indicated, it is necessary to place patients on fall precautions and monitor INR levels every day, as mentioned above.

Characterization and application in yogurt of genipin-crosslinked chitosan microcapsules encapsulating with Lactiplantibacillus plantarum DMDL 9010.

Microcapsules could improve the protection of probiotics in the lyophilization and gastrointestinal digestion process. The purpose of this study was to prepare Lactiplantibacillus plantarum DMDL 9010 (LP9010) microcapsules by cross-linking chitosan with genipin and to determine the encapsulation efficiency, morphological characterization, storage stability and the application of the microcapsules in fermentation. The results showed that the LP9010 microcapsules embedded in 1.00 wt% genipin cross-linked chitosan were in a uniform spherical shape with a smooth surface and satisfying agglomeration. The LP9010 microcapsules demonstrated the reasonable thermal stability and persistence of biological activity in the range of -20 °C to 25 °C. Additionally, yogurt obtained from the ST + LB + ELP9010 strain formulation with the addition of microencapsulated LP9010 had smaller particles, better taste, and better stability compared with the yogurt obtained from other strain formulations. As detected by GC-MS, the yogurt formulated with ST + LB + ELP9010 as a strain retained more flavor substances and the content of flavor substances was greater than that of the yogurt obtained from other strain formulations. Therefore, genipin cross-link chitosan could be a suitable microencapsulated material for producing yogurt fermentation strains.

Challenges Posed by COVID-19 on Doctoral Students' Wellbeing in Russia: Of International and Domestic Doctoral Students, Who Suffers More?

Background: While universities closed, implementing remote teaching and learning in response to COVID-19, this change significantly impacted the lives of graduate students, given their exposure to unique and diverse experiences. It thus has become essential to understand the possible differences in regard to the pandemic's impact on international and domestic students. Purpose: The aim of this study was to explore the consequences of the challenges posed by COVID-19 on doctoral students' wellbeing in Russia. Methods: The study surveyed 4454 doctoral students across 249 Russian public universities. Results: The challenges posed by COVID-19 negatively affected international doctoral students' learning experience (ß= -0.269, p<0.001); students' satisfaction with supervision (ß= -0.098, p<0.001); dissertation experience (ß= -0.039, p<0.001); and doctoral program satisfaction (ß= -0.034, p<0.001). Furthermore, the challenges posed by COVID-19 affected domestic doctoral students' learning experience (ß=-0.368, p<0.001); students' satisfaction with supervision (ß=-0.194, p<0.001) and doctoral program satisfaction (ß=-0.034, p<0.001). However, the influence of the challenges posed by COVID-19 on communication frequency was relatively positive for both international (ß=0.060, p<0.001) and domestic students (ß=0.021, p<0.001), and dissertation experience (ß=0.061, p<0.001) was also positive for only domestic students. Furthermore, controlled factors comprising field of study (ß=-0.033, p<0.001); year of study (ß=0.127, p<0.001); and university region (ß=-0.056, p<0.001) influenced the effect of the challenges posed by COVID-19 on international doctoral students. Conclusion: The COVID-19 challenges had the greatest impact on the wellbeing of international students. Furthermore, both international and domestic students' communication frequency with their supervisors underwent a relatively positive impact (which implies no effect on both categories of students). Furthermore, the challenges posed by COVID-19 had no effect on domestic students' dissertation experiences. Finally, among the controlled variables, field of study, year of study, and university region were discovered to be significant factors in relation to the challenges posed by COVID-19 for international students.

Artificial intelligence-driven malware detection framework for internet of things environment.

The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework's performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study's outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

A comprehensive survey of techniques for developing an Arabic question answering system.

The question-answering system (QAS) aims to produce a response to a query using information from a text corpus. Arabic is a complex language. However, it has more than 450 million native speakers across the globe. The Saudi Arabian government encourages organizations to automate their routine activities to provide adequate services to their stakeholders. The performance of current Arabic QASs is limited to the specific domain. An effective QAS retrieves relevant responses from structured and unstructured data based on the user query. Many QAS studies categorized QASs according to factors, including user queries, dataset characteristics, and the nature of the responses. A more comprehensive examination of QASs is required to improve the QAS development according to the present QAS requirements. The current literature presents the features and classifications of the Arabic QAS. There is a lack of studies to report the techniques of Arabic QAS development. Thus, this study suggests a systematic literature review of strategies for developing Arabic QAS. A total of 617 articles were collected, and 40 papers were included in the proposed review. The outcome reveals the importance of the dataset and the deep learning techniques used to improve the performance of the QAS. The existing systems depend on supervised learning methods that lower QAS performance. In addition, the recent development of machine learning techniques encourages researchers to develop unsupervised QAS.

Epidemiology, Trends, Utilization Disparities, and Outcomes of Catheter Ablation and Its Association With Coronary Vasospasm Amongst Patients With Non-valvular Atrial Fibrillation: A Nationwide Burden of Last Decade.

BACKGROUND: Catheter ablation (CA) is an important curative treatment for non-valvular atrial fibrillation (NVAF), however, nationwide data on its utilization and disparities is limited. Coronary vasospasm is a rare, life-threatening, peri-operative complication of CA with limited literature in Caucasians. METHODS: We performed a retrospective study on adult hospitalizations in the USA from 2007 to 2017 by obtaining the data from National Inpatient Sample. The primary endpoints of our study were to identify the utilization rate of CA, disparities in utilization, and study the outcomes associated with CA. The secondary endpoints of the study were to identify the incidence of coronary vasospasm amongst patients who underwent CA, evaluate their association, and identify the predictors of coronary vasospasm. RESULTS: From 35,906,946 patients with NVAF, 343641 (0.96%) underwent CA. Its utilization decreased from 1% in 2007 to 0.71% in 2017. Patients who underwent CA, compared to those without CA, fared better in terms of hospital length of stay, mortality rate, disability rate, and discharge to the non-home facility. Patients in the 50-75 years age group, Native Americans, those with private insurance, and median household income of 76-100th percentile were associated with higher odds of CA utilization. Urban teaching hospitals and large-bedded hospitals performed more ablations, while the Mid-West region fared lower than the South, the West, and the Northeast. The prevalence of coronary vasospasm was higher amongst CA in comparison without CA, however, in regression analysis, no significant association was demonstrated between CA and coronary vasospasm. CONCLUSION: CA is an important treatment modality that is associated with improved clinical outcomes. Identification of factors associated with lower utilization of CA and its disparities will help to mitigate the burden associated with NVAF.

Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images.

Coronary artery disease (CAD) is one of the major causes of fatalities across the globe. The recent developments in convolutional neural networks (CNN) allow researchers to detect CAD from computed tomography (CT) images. The CAD detection model assists physicians in identifying cardiac disease at earlier stages. The recent CAD detection models demand a high computational cost and a more significant number of images. Therefore, this study intends to develop a CNN-based CAD detection model. The researchers apply an image enhancement technique to improve the CT image quality. The authors employed You look only once (YOLO) V7 for extracting the features. Aquila optimization is used for optimizing the hyperparameters of the UNet++ model to predict CAD. The proposed feature extraction technique and hyperparameter tuning approach reduces the computational costs and improves the performance of the UNet++ model. Two datasets are utilized for evaluating the performance of the proposed CAD detection model. The experimental outcomes suggest that the proposed method achieves an accuracy, recall, precision, F1-score, Matthews correlation coefficient, and Kappa of 99.4, 98.5, 98.65, 98.6, 95.35, and 95 and 99.5, 98.95, 98.95, 98.95, 96.35, and 96.25 for datasets 1 and 2, respectively. In addition, the proposed model outperforms the recent techniques by obtaining the area under the receiver operating characteristic and precision-recall curve of 0.97 and 0.95, and 0.96 and 0.94 for datasets 1 and 2, respectively. Moreover, the proposed model obtained a better confidence interval and standard deviation of [98.64-98.72] and 0.0014, and [97.41-97.49] and 0.0019 for datasets 1 and 2, respectively. The study's findings suggest that the proposed model can support physicians in identifying CAD with limited resources.

Geochemical characterisation and geostatistical evaluation of groundwater suitability: a case study in Perambalur District, Tamil Nadu, India.

Groundwater is a naturally occurring potential source for drinking, irrigation, agricultural and industrial purposes. The population growth and accelerated development of industries and agriculture activity degrade groundwater quality. The groundwater quality of an area was determined by the physical and chemical parameters, influenced by geology, soil, land use, land cover and anthropogenic activities. Perambalur district in Tamil Nadu has been selected as a study area with a total geographical area of around 1757 km2. In the study area, groundwater quality decreases due to the usage of chemical fertilisers and pesticides in agricultural land and mining activities. So, the hydrogeochemical assessment will help to determine the groundwater suitability for drinking. Forty-eight groundwater samples were collected from the study area during the pre-monsoon (July 2021) and post-monsoon season (January 2022). Samples were analysed using the standard methods prescribed by the American Public Health Association for pH, electrical conductivity (EC), total dissolved solids (TDS), calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride, sulphate, nitrate and fluoride. The spatial distribution of major physiochemical parameters is mapped using the inverse distance weighted (IDW) interpolation technique. The evaluation of hydrochemical facies from piper plots revealed that the major cation and anion were in the order of Ca2+ > Mg2+ > Na+ > K+ and Cl- > HCO3- > SO42- > NO3- in both seasons, respectively. Further, the plot explains the presence of both permanent and temporary hardness in the groundwater. The evaluation of hydrochemical facies from the piper plot emphasises that the reverse ion exchange controls groundwater chemistry. The assessment of chloro-alkaline indices reveals that the sodium and potassium in groundwater get substituted with magnesium and calcium in the parent rock, which determines the groundwater composition. The values of saturation indices reveal that calcite and dolomite are supersaturated and tend to precipitate. From principal component analysis, the principal components have an eigenvalue of more than 1, containing 79.8% and 79.2% in the total variance in pre-monsoon and post-monsoon, respectively. Most physiochemical parameters like TDS, EC, Na+, Mg2+, Cl- and SO42 - have strong positive loading and are responsible for the changes in groundwater chemistry. Finally, the calculation of the water quality index identified that groundwater quality in post-monsoon tends to decline compared to pre-monsoon.

Clinical and radiological outcomes of SARS-CoV-2 related organising pneumonia in COVID-19 survivors.

INTRODUCTION: COVID-19 patients frequently demonstrate radiological organising pneumonia (OP) pattern. The longterm outcome and treatment options for this group of patients remain uncertain. We aim to describe the clinical and radiological outcomes of patients with COVID-19-related OP and identify possible clinical factors associated with inferior radiological outcome. MATERIALS AND METHODS: Post-COVID-19 clinic attendees, consisting of post-COVID-19 patients discharged from major hospitals in the state of Selangor during the third pandemic wave of COVID-19 in Malaysia, were enrolled in this retrospective study for 6 months. Physician-scored Modified Medical Research Council (mMRC), patient self-reported quality of life (EQ-VAS) score and follow-up CT scan were evaluated. RESULTS: Our cohort comprised 131 patients, with a median age of 52 (IQR 39-60) years and median BMI of 29.40 (IQR 25.59-34.72). Majority (72.5%) had co-morbidities, and 97.7% had severe disease requiring supplementary oxygen support during the acute COVID-19 episode. 56.5% required intensive care; among which one-third were invasively ventilated. Median equivalent dose of methylprednisolone prescribed was 2.60 (IQR 1.29-5.18) mg/kg during admission, while the median prednisolone dose upon discharge was 0.64 (IQR 0.51-0.78) mg/kg. It was tapered over a median of 8.0 (IQR 5.8-9.0) weeks. Upon follow-up at 11 (IQR 8-15) weeks, one-third of patients remained symptomatic, with cough, fatigue and dyspnoea being the most reported symptoms. mMRC and EQ-VAS scores improved significantly (p<0.001) during follow-up. Repeat CT scans were done in 59.5% of patients, with 94.8% of them demonstrating improvement. In fact, 51.7% had complete radiological resolution. Intensive care admission and mechanical ventilation are among the factors which were associated with poorer radiological outcomes, p<0.05. CONCLUSION: Approximately one-third of patients with SARSCoV- 2-related OP remained symptomatic at 3 months of follow-up. Majority demonstrated favourable radiological outcomes at 5-month reassessment, except those who required intensive care unit admission and mechanical ventilation.