Management of Length Unstable Femur Fractures in Children by Flexible Intramedullary Nails: A Systematic Review.

PURPOSE OF THE STUDY: Surgical options for paediatric femoral fractures include fl exible intramedullary nailing (FIN), plating, and external fi xators. Length unstable fractures are usually spiral, long oblique, or comminuted and are often associated with > 2 cm of shortening. The purpose of this study was to see whether FIN is effective for managing unstable femur fractures in children. MATERIAL AND METHODS: An electronic literature search was performed up to 25 February 2022 in Cochrane Library, PubMed, and Embase databases using a combination of MeSH search terms and keywords related to the population (e.g., "child" AND "diaphyses" AND "femur"), and intervention (e.g., "nail" OR "ESIN"). The data extracted included the study details, Demographic data, surgical details, postoperative immobilization, complications, and outcome. RESULTS: Eight studies with a total sample size of 369 patients were reviewed. The mean operative time, blood loss, and length of stay in the hospital were 67.62±12.32 minutes, 33.82±16.82 ml, and 4.9±1.27 days, respectively. The results were excellent in 61.92% of the patients, satisfactory in 32.61%, and poor in 5.43%. 4.54% of patients had major complications requiring reoperation and 32.46% of patients had minor complications. the most common complication was nail prominence seen in 26.30% of patients. Locked Ender's nail was associated with the least reoperation, malunion, and LLD rate compared to other types of FIN. CONCLUSIONS: FIN along with a single walking spica cast is a good choice in all forms of paediatric femoral fracture patterns allowing proper alignment and rotation. Locked Ender's nail is safe and effective for managing unstable paediatric femur fracture. KEY WORDS: pediatric femur fracture, length unstable, fl exible intramedullary nailing, submuscular plating, Flynn criterion.

Community sensitization to improve acceptability of indoor residual spraying (IRS) in Mewat district of Haryana, India: A community-based interventional study.

BACKGROUND & OBJECTIVES: Indoor Residual Spraying (IRS) is an important part of malaria prevention and control strategies for villages with annual parasitic incidence (API) of more than 5, or other criteria by National Program of the country. At the time of conception of the study, i.e., in 2012, district Mewat in Haryana state, India was malaria endemic including 35 malaria high risk villages with API more than 5. The current study aimed to improve acceptability of IRS among the residents of Mewat district. METHODS: This was a community based interventional study. Out of the 14 sub-centers, there were 12 sub-centers catering the population of 79 villages, out of which 35 villages had API of more than five, and hence, fulfilled the criteria for regular IRS. Considering sub-center as the unit of randomization, out of all the 12 sub-centers which cater population of villages with API > 5, equal number of the sub-centers matched according to the population size of those villages were assigned to the intervention and non-intervention (control) groups by randomization using lottery technique. The intervention comprised community sensitization through community meetings, wall paintings and distribution of pamphlets. Baseline and post intervention acceptability of IRS was measured and compared. RESULTS: There was 21.7% (95% CI 16.12 to 27.15) increase in intention to accept IRS as per guidelines, and 15.2% (95% CI 9.50 to 20.80) increase in the actual acceptance of IRS in intervention group of villages in Mewat district. INTERPRETATION & CONCLUSION: The intervention can bring about significant increase in the acceptability of IRS which is an important component of malaria control under national programme in district Mewat, Haryana, India.

Genomic and functional characterization of coleopteran insect-specific α-amylase inhibitor gene from Amaranthus species.

The smallest 32 amino acid α-amylase inhibitor from Amaranthus hypochondriacus (AAI) is reported. The complete gene of pre-protein (AhAI) encoding a 26 amino acid (aa) signal peptide followed by the 43 aa region and the previously identified 32 aa peptide was cloned successfully. Three cysteine residues and one disulfide bond conserved within known α-amylase inhibitors were present in AhAI. Identical genomic and open reading frame was found to be present in close relatives of A. hypochondriacus namely Amaranthus paniculatus, Achyranthes aspera and Celosia argentea. Interestingly, the 3'UTR of AhAI varied in these species. The highest expression of AhAI was observed in A. hypochondriacus inflorescence; however, it was not detected in the seed. We hypothesized that the inhibitor expressed in leaves and inflorescence might be transported to the seeds. Sub-cellular localization studies clearly indicated the involvement of AhAI signal peptide in extracellular secretion. Full length rAhAI showed differential inhibition against α-amylases from human, insects, fungi and bacteria. Particularly, α-amylases from Helicoverpa armigera (Lepidoptera) were not inhibited by AhAI while Tribolium castaneum and Callosobruchus chinensis (Coleoptera) α-amylases were completely inhibited. Molecular docking of AhAI revealed tighter interactions with active site residues of T. castaneum α-amylase compared to C. chinensis α-amylase, which could be the rationale behind the disparity in their IC50. Normal growth, development and adult emergence of C. chinensis were hampered after feeding on rAhAI. Altogether, the ability of AhAI to affect the growth of C. chinensis demonstrated its potential as an efficient bio-control agent, especially against stored grain pests.

Biological Activities and Identification of Bioactive Metabolite from Endophytic Aspergillus flavus L7 Isolated from Aegle marmelos.

Aegle marmelos, a well-known Indian plant with medicinal and religious importance, has been extensively used in Indian traditional medicine. The present study aimed to isolate, identify, and evaluate the biological activities of endophytic fungi from A. marmelos. One of the isolates, labeled as L7, was identified as Aspergillus flavus using morphology and ITS gene sequence. Total phenolic and flavonoid contents in the culture filtrate were found to be 65.77 mg GAE/ml and 158.33 mg quercetin/ml of crude extract, respectively. The extract showed excellent antimicrobial activity against common human bacterial and fungal pathogens. The test extract at 700 µg/ml, which notably reduced the concentration of DPPH-free radical as percent DPPH scavenging activity, was found to be the highest (64.53 %). The extract, at the concentration of 2 mg/ml, produced 70 % inhibition of hemolysis of RBCs compared to 78 % produced by standard drug (Ibuprofen). Chemical profiling of the fermented extract using TLC followed by UV and FTIR revealed the presence of flavonoids. The HPLC analysis confirmed the presence of bioflavonoid rutin in the extract. To the best of our knowledge, this is the first report on production of bioactive flavonoid by endophytic Aspergillus flavus obtained from A. marmelos and its pharmaceutical potential. In conclusion, the endophytic Aspergillus flavus obtained from the A. marmelos could be explored as an economic and potential natural resource with diverse pharmaceutical and biological activities.

The Prevalence of Frailty and Associated Factors, Including Food Security in Community Dwelling Older Adults with Multimorbidity: A Cross-Sectional Analysis from the Longitudinal Aging Study in India.

BACKGROUND: The global increase in multimorbidity among older adults is a result of ongoing epidemiological and demographic transitions. This study focuses on the prevalence and determinants of frailty in this demographic in India, accounting for the potential mediating role of food insecurity. OBJECTIVES: To determine the prevalence and determinants of frailty among older Indian adults with multimorbidity, and to ascertain the mediating effect of food insecurity on frailty. DESIGN: Cross-sectional analysis of cohort study data. SETTING: Analysis of data from the Longitudinal Aging Study in India (LASI) Wave 1 (2017-2018). PARTICIPANTS: 31,902 individuals aged 60 and above of whom 7900 were categorized as having multimorbidity. MEASUREMENTS: Frailty was assessed using a modified Fried scale. Details on sociodemographic factors, lifestyle choices, and health-related variables were collected through face-to-face participant interviews. Multimorbidity was defined as the presence of two or more chronic conditions such as hypertension, diabetes, cancer, chronic lung disease, chronic heart disease, stroke, bone disease, neurological or psychiatric problems, and high cholesterol. Statistical analysis was conducted using Stata 15.1. RESULTS: The weighted prevalence of frailty in individuals with multimorbidity was 30.31% (95% CI: 28.17, 32.54), significantly higher than those without multimorbidity (23.81%, 95% CI: 22.90, 24.74) (P<0.001). Frailty prevalence was higher in women (33.27%) than in men (26.56%) among those with multimorbidity. In the group with multimorbidity, age ≥75 (years), middle MPCE quintile, lower educational attainment, unemployment, and low body mass index was associated with higher odds of frailty. Mediation analysis showed that 3.47% of the association between multimorbidity and frailty was mediated by food insecurity. CONCLUSION: Frailty is prevalent among older adults with multimorbidity in India, with significant disparities based on gender, age, socioeconomic status, and body mass index. Food insecurity partially mediates the relationship between multimorbidity and frailty, highlighting the need for targeted interventions addressing both health and nutritional insecurities in this population.

Genotoxicity of Formaldehyde: Effect of Whole-Body Exposure on Polychromatic Erythrocyte/Normochromatic Erythrocyte Ratio in Male and Female Rats.

Every day, millions of individuals are exposed to formaldehyde (FA) due to its extensive presence and versatile use. Many in vivoand in vitroexperiments revealed that the mechanism of genotoxicity induced by FA exposure is complex yet toxicity upon whole-body exposure (WBE) to FA is less. As teachers, students, and skilled assistants in the health care sectors are also extensively exposed to FA vapors, it might result in genotoxicity. However, the effects of subchronic exposure to FA at low concentrations are not clear. Hence, analysis of the micronucleus (MN) was necessary to study the genetic toxicity triggered by FA in the bone marrow of male and female experimental rats. The present study is a gender- and duration of exposure-based assessment of the geno- and cytotoxicity in bone marrow cells of Wistar rats to study the effect of WBE to 10% FA on polychromatic erythrocytes/normochromatic erythrocytes (PCE/NCE) ratio and micronucleated polychromatic erythrocytes (MnPCE) in experimental rats. The obtained result clearly showed that WBE to FA for 60 days at concentrations between 1 and 1.1 ppm (0, 1, and 1.5 h) induced genotoxic effects in both male and female rats by altering the MnPCE% and significantly increasing the ratio of PCE/NCE (1.07 ± 0.23, 1.20 ± 0.20, 1.22 ± 0.14). The PCE/NCE ratio in male rats was lesser (0.98, 1.12, and 1.18) when compared with female rats (1.17, 1.29, and 1.26) with 0, 1, and 1.5 h exposure, respectively. Thus, the genetic/cellular sensitivity to FA differs among the sexes and also depends on the exposure duration.

Design and Analysis of Multilayered Neural Network-Based Intrusion Detection System in the Internet of Things Network.

A large array of objects is networked together under the sophisticated concept known as the Internet of Things (IoT). These connected devices collect crucial information that could have a big impact on society, business, and the entire planet. In hostile settings like the internet, the IoT is particularly susceptible to multiple threats. Standard high-end security solutions are insufficient for safeguarding an IoT system due to the low processing power and storage capacity of IoT devices. This emphasizes the demand for scalable, distributed, and long-lasting smart security solutions. Deep learning excels at handling heterogeneous data of varying sizes. In this study, the transport layer of IoT networks is secured using a multilayered security approach based on deep learning. The created architecture uses the intrusion detection datasets from CIC-IDS-2018, BoT-IoT, and ToN-IoT to evaluate the suggested multi-layered approach. Finally, the new design outperformed the existing methods and obtained an accuracy of 98% based on the examined criteria.

Psychological Morbidity Among COVID-19 Survivors: A Cross-Sectional Study Among Health Care Workers.

Objective: A range of psychiatric morbidities such as persistent depression, anxiety, insomnia, and posttraumatic stress disorder (PTSD) has been observed in coronavirus disease 2019 (COVID-19) survivors. The objective of this study was to explore the psychological status of health care workers after recovery from COVID-19 and to examine the sociodemographic and clinical factors associated with psychiatric morbidity.Methods: A cross-sectional study was conducted among health care workers of a tertiary care hospital in South India. The study included health care workers who tested positive for COVID-19 according to the provisional guidelines of the World Health Organization. The data were collected after they tested negative for COVID-19 from September 2020 to October 2020. The study used a semistructured proforma and rating scales such as the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, and Posttraumatic Stress Disorder Checklist for DSM-5 to assess for depression, anxiety, and PTSD.Results: The results indicate that the prevalence of depression, anxiety, and PTSD among 107 post-COVID patients was 26.2%%, 12.1%, and 3.7%%, respectively. Female sex (P = .017), patients with post-COVID persistent physical symptoms (P = .05), and the duration of fever during the acute phase of COVID-19 infection (P = .005) were found to have a statistically significant association with a higher rate of depression among the study population.Conclusions: The study findings indicate that all COVID-19 survivors working in the health care sector should be screened for depression and anxiety disorders regularly for early detection and effective management.

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images.

The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images.

The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis. This study employs multiconvolutional transfer learning (MCTL) for applying deep learning to small medical imaging datasets in an effort to address these issues. Multiconvolutional transfer learning is a model based on transfer learning that enables deep learning with small datasets. In order to learn new features on a smaller target dataset, an initial baseline is used in the transfer learning process. In this study, 3D MRI images of brain tumors are classified using a convolutional autoencoder method. In order to use unenhanced Magnetic Resonance Imaging (MRI) for clinical diagnosis, expensive and invasive contrast-enhancing procedures must be performed. MCTL has been shown to increase accuracy by 1.5%, indicating that small targets are more easily detected with MCTL. This research can be applied to a wide range of medical imaging and diagnostic procedures, including improving the accuracy of brain tumor severity diagnosis through the use of MRI.

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning.

The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases.

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis.

As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms. In the battle against the virus, however, researchers and decision-makers must contend with an ever-increasing volume of data, referred to as "big data." VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine-tune our model on lung ultrasound frames from healthy people as well as from patients with COVID-19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non-COVID-19. COVID-19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID-19.

Identification of novel bioactive molecules from garlic bulbs: A special effort to determine the anticancer potential against lung cancer with targeted drugs.

Garlic (Allium sativum L.), is a predominant spice, which is used as an herbal medicine and flavoring agent, since ancient times. It has a rich source of various secondary metabolites such as flavonoids, terpenoids and alkaloids, which have various pharmacological properties. Garlic is used in the treatment of various ailments such as cancer, diabetes and cardiovascular diseases. The present study aims to explore the plausible mechanisms of the selected phytocompounds as potential inhibitors against the known drug targets of non-small-cell lung cancer (NSCLC). The phytocompounds of garlic were identified by gas chromatography-mass spectrometry (GC-MS) technique. Subsequently, the identified phytocompounds were subjected to molecular docking to predict the binding with the drug targets, epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) and group IIa secretory phospholipase A2 (sPLA2-IIA). Molecular dynamics is used to predict the stability of the identified phytocompounds against NSCLC drug targets by refining the intermolecular interactions formed between them. Among the 12 phytocompounds of garlic, three compounds[1,4-dimethyl-7-(1-methylethyl)-2-azulenyl]phenylmethanone, 2,4-bis(1-phenylethyl)-phenol and 4,5-2 h-oxazole-5-one,4-[3,5-di-t-butyl-4-methoxyphenyl] methylene-2-phenyl were identified as potential inhibitors, which might be suitable for targeting the different clinical forms of EGFR and dual inhibition of the studied drug targets to combat NSCLC. The result of this study suggest that these identified phytocompounds from garlic would serve as promising leads for the development of lead molecules to design new multi-targeting drugs to address the different clinical forms of NSCLC.

Plant metabolites: an alternative and sustainable approach towards post harvest pest management in pulses.

Grain legumes are an important source of proteins in vegetarian diet besides their role in biological nitrogen fixation. They are prone to heavy pest infestation both on and off the field. Pest associated losses are an important contributing factor towards declining per capita availability of grain legumes. Synthetic chemical pesticides have played an important role in crop preservation, however their incessant use has posed several environmental and human health concerns. Methyl bromide and phosphine are commonly used for the post harvest preservation of grain legumes. However, the former has to be phased out by 2015 as per the Montreal protocol whereas the latter is showing development of resistance to it by the insects. In the light of this, alternative, safer and sustainable strategies are needed for crop protection. Plants can serve as a rich source of bioactive chemicals for this purpose. Both primary as well as secondary metabolites can be evaluated against the target pests. The paper reviews the status of research in the area of use of plant metabolites in post harvest pest management of grain legumes.

Extraskeletal osteogenic sarcoma: a rare entity.

A 25-year-old female presented with a rapidly increasing painful swelling around right lower thigh and knee for the last 5 months with distant metastasis in the form of lung involvement. X-ray of the part showed only soft tissue swelling without any bony involvement. A diagnosis of extraskeletal osteosarcoma was made which was confirmed by immunohistochemistry.

Characterization of two coleopteran α-amylases and molecular insights into their differential inhibition by synthetic α-amylase inhibitor, acarbose.

Post-harvest insect infestation of stored grains makes them unfit for human consumption and leads to severe economic loss. Here, we report functional and structural characterization of two coleopteran α-amylases viz. Callosobruchus chinensis α-amylase (CcAmy) and Tribolium castaneum α-amylase (TcAmy) along with their interactions with proteinaceous and non-proteinaceous α-amylase inhibitors. Secondary structural alignment of CcAmy and TcAmy with other coleopteran α-amylases revealed conserved motifs, active sites, di-sulfide bonds and two point mutations at spatially conserved substrate or inhibitor-binding sites. Homology modeling and molecular docking showed structural differences between these two enzymes. Both the enzymes had similar optimum pH values but differed in their optimum temperature. Overall, pattern of enzyme stabilities were similar under various temperature and pH conditions. Further, CcAmy and TcAmy differed in their substrate affinity and catalytic efficiency towards starch and amylopectin. HPLC analysis detected common amylolytic products like maltose and malto-triose while glucose and malto-tetrose were unique in CcAmy and TcAmy catalyzed reactions respectively. At very low concentrations, wheat α-amylase inhibitor was found to be superior over the acarbose as far as complete inhibition of amylolytic activities of CcAmy and TcAmy was concerned. Mechanism underlying differential amylolytic reaction inhibition by acarbose was discussed.

Unraveling the relationship between mortality, hyponatremia, inflammation and malnutrition in hemodialysis patients: results from the international MONDO initiative.

BACKGROUND/OBJECTIVES: Hyponatremia is a risk factor for mortality in hemodialysis (HD) patients. It is not well known to which extent the comorbidities, malnutrition, fluid status imbalance and inflammation are related to hyponatremia and affect outcomes. SUBJECTS/METHODS: We studied 8883 patients from the European subset of the international MONitoring Dialysis Outcomes initiative. Nutritional and fluid statuses were assessed by bioimpedance spectroscopy. Fluid depletion was defined as overhydration⩽-1.1 l and fluid overload as overhydration>+1.1 l, respectively. Malnutrition was defined as a lean tissue index below the 10th percentile of age- and gender-matched healthy controls. Hyponatremia and inflammation were defined as serum sodium levels <135 mEq/l and C-reactive protein levels>6.0 mg/l, respectively. We used logistic regression to test for predictors of hyponatremia and Cox proportional hazards analysis to assess the association with all-cause mortality. RESULTS: Hyponatremia was predicted by the presence of malnutrition (odds ratio (OR)=1.49 (95% confidence interval (CI)=1.30-1.70), inflammation (OR=1.44 (95% CI=1.26-1.64)) and fluid overload ((>+1.1 l to +2.5 l) OR=0.73 (95% CI=0.62-0.85)) but not by fluid depletion (OR=1.34 (95% CI=0.92-1.96)). Malnutrition, inflammation, fluid overload, fluid depletion and hyponatremia (hazard ratio=1.70 (95% CI=1.46-1.99)) were independent predictors for all-cause mortality. CONCLUSIONS: In HD patients, hyponatremia is associated with malnutrition, inflammation and fluid overload. Hyponatremia maintained predictive for all-cause mortality after adjustment for malnutrition, inflammation and fluid status abnormalities. The presence of hyponatremia may assist in identifying HD patients at increased risk of death.

QT dispersion as a marker of left ventricular mass in essential hypertension.

Increased left ventricular mass is an important risk factor in hypertension for various cardiovascular complications. We studied 49 patients of essential hypertension and the relationship between QT dispersion (defined as the difference between the maximum and the minimum QT interval in a 12-lead electrocardiogram) and M-mode echocardiographic left ventricular mass was analysed. Criterion of left ventricular hypertrophy was left ventricular mass index above 134 gm/m2 in men and above 110 gm/m2 in women. There were a total of 22 patients with left ventricular hypertrophy (15 men and 7 women). The mean QT dispersion was 82.66 +/- 35.34 milliseconds (ms) in men with left ventricular hypertrophy and 36.66 +/- 15.71 ms in men without left ventricular hypertrophy. The mean QT dispersion was 77.14 +/- 29.27 ms in women with left ventricular hypertrophy and 26.66 +/- 9.99 ms in women without left ventricular hypertrophy. The correlation co-efficient was 0.59 (p < 0.001) and 0.69 (p < 0.01) in men and women, respectively. Hence, we conclude that there is a direct, linear and positive correlation between left ventricular mass and QT dispersion in essential hypertension.