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Background The nationwide lockdown (March 25 to June 8, 2020) to curb the spread of coronavirus infection had significant health and economic impacts on the Indian economy. There is limited empirical evidence on how COVID-19 restrictive measures may impact the economic welfare of specific groups of patients, e.g., tuberculosis patients. We provide the first such evidence for India. Methods A total of 291 tuberculosis patients from the general population and from a high-risk group, patients from tea garden areas, were interviewed at different time points to understand household income loss during the complete lockdown, three and eight months after the complete lockdown was lifted. Income loss was estimated by comparing net monthly household income during and after lockdown with prelockdown income. Tuberculosis service utilization patterns before and during the lockdown period also were examined. Household income loss, travel and other expenses related to tuberculosis drug pickup were presented in 2020 US dollars (1 US$ = INR 74.132). Results 26% of households with tuberculosis patients in tea garden areas and 51% of households in the general population had zero monthly income during the complete lockdown months (April–May 2020). Overall income loss slowly recovered during July–August compared to April–May 2020. Approximately 7% of patients in the general population and 4% in tea garden areas discontinued their tuberculosis medicines because of the complete lockdown. Conclusion Discontinuation of medicine will have an additional burden on the tuberculosis elimination program in terms of additional cases, including multidrug resistant tuberculosis cases. Income loss for households and poor restoration of income after the lockdown will likely have an impact on the nutrition of tuberculosis patients and families. Tuberculosis patients working in the informal sector were the worst affected group during the nationwide lockdown. This emphasizes that a policy priority must continue to protect those working in informal sectors from the economic consequences of such restrictive measures, including paid sick leave, additional food support, and direct benefit transfers. Alongside ensuring widespread access to COVID-19 vaccines, these policy actions remain pivotal in ensuring the well-being of those who are unfortunate enough to be living with tuberculosis.

Congenital myotonia represents a rare group of genetically inherited conditions. It can be either autosomal dominant (Thomsen) or autosomal recessive (Becker). It is characterized by muscular hypertrophy, proximal weakness, and myotonia, or impaired relaxation after contraction. These are due to mutations in the CLC1 gene. A 14-year-old male child presented with complaints of gradually progressive weakness for five years. Weakness was more pronounced in the proximal muscle groups. The weakness worsened after rest and improved with activity. This led to absenteeism and affected his school performance. Clinical examination showed generalized muscular hypertrophy with pronounced hypertrophy of the calf muscles. A neurological examination showed significant myotonia and impaired relaxation after making a fist. The diagnosis of myotonia was confirmed by electromyography, which produced a dive-bomber sound on insertion. Next-generation sequencing revealed a homozygous eight-base pair insertion in exon 19 of the CLCN1 gene. This mutation has not been reported in the existing literature for myotonia congenita. The child was started on mexiletine and improved significantly. Presently, the patient is on regular medications and doing well on follow-up. Though rare, congenital myotonia is an important cause of neuromuscular weakness. It can be easily diagnosed with a thorough clinical examination and routine testing for myotonia in all children with weakness. The treatment is relatively simple and can give the patient significant relief. Myotonia can be easily diagnosed clinically, and pharmacotherapy and proper monitoring can remarkably improve patients' quality of life.

The increased risks of storm flood occurrences in large cities are the result of land use changes due to rapid urbanization. This study examines the influence of land use changes in Khulna City Corporation (KCC) area on surface runoff over a period of 15 years, from 2005 to 2020. Land use–land cover (LULC) maps for 2005, 2010, 2015, and 2020 were created employing support vector machine (SVM)-based supervised image classification using time-series satellite data, and the surface runoff was determined using Soil Conservation Service-Curve Number model. The major land use change drivers of surface runoff were determined through a correlation analysis. Surface runoff was observed to follow a similar trend as that of impervious urban areas, which went up by 5.44% from 2005 to 2020 (17.00 mm increment in average runoff) and the opposite trend was found in vegetation land cover, which declined by 13.34% in areal extent throughout the study period. In comparison with other types of land use, surface runoff changes were most significantly associated with the changes in urban impervious areas and vegetation land use-land cover (LULC) class. In fast-growing cities across the world, and especially in developing nations, the results of this study may serve as a guide for urban storm flood management and urban planning efforts.

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

Background and objective The incidence of adverse drug reactions (ADRs) in hospitalized children varies from 0.6-16.8%. There is a lack of uniformity and an absence of quality reporting with respect to the collection of data on ADRs worldwide, resulting in a scarcity of data regarding ADRs in children. In light of this, we aimed to analyze various factors related to ADRs in the pediatric population in the ADR Monitoring Center (AMC) of a teaching hospital in Odisha, India. Methods This was a record-based study conducted by the department of pharmacology in collaboration with the department of pediatrics. Detailed information regarding all ADR cases in children (<14 years of age) was collected in a format designed by the Indian Pharmacopoeia Commission (IPC). A total of 105 ADRs reported during a five-year period (2015-20) were subjected to analysis. Results The largest number of ADRs were reported in the age group zero to five years (41%). Males were affected more compared to females (1.7:1). Cutaneous ADRs were the most common type (86.5%) followed by the involvement of the gastrointestinal system (10%); 21% of cases were serious in nature, i.e., they required either hospitalization or led to a prolonged hospital stay. Antibiotics were the major drug category involved in causing drug reactions (66%) and among them, ceftriaxone (24.6%) was the most common causative agent. Conclusions One-fifth of the pediatric cases of ADRs were serious in nature. The most common causative agent was antibiotics, especially beta-lactams. There is an urgent need to raise awareness among healthcare professionals by conducting training programs to encourage the spontaneous reporting of ADRs, which will help to ensure drug safety in the pediatric population.

Kasabach-Merritt syndrome is a rare vascular tumor usually seen in infancy. It is locally aggressive and infiltrates the skin, subcutaneous tissue, and muscles. It is characterized by coagulopathy with thrombocytopenia, microangiopathic hemolytic anemia, and raised d-dimer levels. In small infants, this can cause life-threatening bleeding and can be fatal. We report on a two-month-old female child who presented to us with a rapidly enlarging purplish swelling on the right arm. It was also associated with petechial spots all over the body. A clinical diagnosis of hemangioma with Kasabach-Merritt phenomenon was made and was further confirmed by hematological investigations which showed anemia, and thrombocytopenia with hypofibrinogenemia. Imaging of the limb confirmed the diagnosis. After taking parental consent, the baby was started on injections of vincristine weekly with oral prednisolone. There was a significant reduction in the tumor’s size and improved blood parameters. After 6 weeks of steroid therapy, the medication was tapered and the child was changed to single agent sirolimus with monitoring of serum levels. There was a remarkable response to sirolimus with complete resolution of the tumour and Kasabach-Merritt phenomenon. Kasabach-Merritt syndrome can be a life-threatening complication in infants. Appropriate pharmacological therapy with stringent monitoring can bring complete resolution.

This study used the Ansys Fluent® computational fluid dynamics code in conjunction with a volume of fluid multiphase model and phase-change model to analyze the flow boiling evaporation heat transfer coefficient and flow patterns of R32 and R1234yf with Al2O3 nanoparticle through the U-bend tube with a curvature ratio for downward-oriented flow. The volume of fluid (VOF) model was used to follow the patterns at the interface, while the SST k-omega model was used to simulate the gas-liquid flow. This work has been validated by utilizing a R134a refrigerant. Simulations were performed at various mass fluxes, vapor qualities, and temperatures to determine the effects of these variables on heat transfer and frictional pressure decrease in the tube. R1234yf shows much better performance than the other pure refrigerants in terms of heat transfer and vaporization. The addition of nanoparticle Al2O3 with the refrigerants R32 and R1234yf significantly improved the heat transfer coefficient and increased the vapor fraction. The frictional pressure drop increases with increasing mass flux and decreases with increasing vapor quality due to a significant decrease in the liquid film thickness. The heat transfer coefficient, on the other hand, increases with increasing mass flux and decreases with vapor quality up to a point. There are certain changes in the heat transfer coefficient at the bend. After the bend, the frictional pressure drop increased at a higher rate than before the bend, and the vapor fraction increased at a higher rate.

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km 2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km 2 ) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km 2 ) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

This cross-sectional study analyzed the pulmonary function in children with sickle cell disease (SCD), assessing the pulmonary abnormalities and comparing these with a control group of children with other forms of anemia. This study was conducted from July 2022 to June 2024 at Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha, India, and included 126 children (63 with SCD, 63 with other forms of anemia) aged between six to 18 years. Anthropometric data, clinical history, and laboratory parameters were collected, and pulmonary function tests (PFTs) were performed using spirometry to evaluate forced vital capacity (FVC) and forced expiratory volume in one second (FEV1). Children with SCD had lower height and weight than those in the control group, with height differences reaching statistical significance. Hemoglobin levels were higher in SCD cases than those in the control group, despite both groups exhibiting anemia. The PFTs revealed lower mean FVC and FEV1 in SCD patients, with a predominant restrictive pattern observed in 40 children with SCD (63.4%) compared to 25 children (39.6%) in controls. Only 17 children with SCD (29.6%) showed normal PFT results. Among risk factors, older age, history of vaso-occlusive crises, acute chest syndrome, and blood transfusions were linked to restrictive abnormalities, though not statistically significant. Children on hydroxyurea showed a trend toward restrictive PFT patterns. The study underscores the high prevalence of restrictive lung function abnormalities in children with SCD, highlighting the importance of regular pulmonary monitoring and early intervention to address pulmonary complications. Further research is warranted to explore the impact of hydroxyurea and the cumulative effect of vaso-occlusive events on lung function in SCD.

Aim To find the diagnostic utility of the neutrophil to lymphocyte ratio (NLR) in the early diagnosis of neonatal sepsis. Methodology The case records of all blood culture-positive septic neonates admitted from January 2018 to December 2018 were reviewed. Total leucocyte count, absolute neutrophil count, absolute lymphocyte counts, NLR, and C-reactive protein (CRP) of septic neonates were compared with gestational age-matched nonseptic neonates by an unpaired t-test. The diagnostic performance of NLR and CRP was analyzed by receiver operating characteristic (ROC) analysis. Result A total of 41 blood culture-positive neonates and 52 nonseptic neonates were enrolled in this study. There was no significant difference in the total leucocyte count and absolute neutrophil counts of septic and nonseptic neonates. The mean absolute lymphocyte count of septic neonates (2795±1424/cumm) was significantly lower than that of nonseptic neonates (4449±1794/cumm; p=<0.001). The mean NLR of septic neonates (3.88±1.78) was significantly higher as compared to nonseptic (2.3404 ±1.98) neonates (p=0.045). For the diagnosis of sepsis, NLR at cutoff >1.7 had a sensitivity and specificity of 68.3% and 46.2%, respectively; CRP at cutoff >6 mg/dl had sensitivity and specificity of 78.05% and 92.31%, respectively. In the ROC analysis, the area under the curve (AUC) for CRP and NLR for the diagnosis of neonatal sepsis was 0.918 (p=<0.001) and 0.623 (p=0.042), respectively. Conclusion Blood culture-positive septic neonates had significantly higher NLR as compared to nonseptic neonates. However, when compared to CRP, NLR was not found to be a better predictor of sepsis in our study.