Where there is no weighing scale:Newborn nourishment and care in Pakistani Punjab

A third of babies in South Asia are born low birthweight, more than in sub-Saharan Africa. This epidemiological enigma has been linked to gender and generational inequalities and to poor health and nutrition over the whole of women’s lives. High rates of breastfeeding initiation are accompanied by high rates of colostrum avoidance, the giving of prelacteal feeds and early supplementation with formula or animal milks as well as other substances. Meanwhile, in Pakistan – despite the extensive presence of public community maternal and child health workers – very few babies are weighed at birth. This paper draws on an ethnographic study conducted in 2014-16 in rural and urban Punjab, to shed light on the interpretation, nourishment and care of newborns who are identified to be kamzoor (weak), and to comment on the extent to which carers’ efforts are influenced by community health workers, who are charged with spreading modern biomedical knowledge and practices. Kamzoori is understood to be caused by maternal depletion and is managed very simply at home by augmenting breastfeeding and giving supplementary milks, and by keeping the baby warm and massaged. In cases where weak newborns do not recover weight, spiritual explanations are invoked and treated through a variety of home remedies/methods. There are often similarities between the interpretations of mothers, grandmothers, and health workers. The paper therefore considers health workers to be engaged in complex cultural translations

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection

Outcomes and prognostic factors in parotid gland malignancies: A 10-year single center experience.

Objectives:To describe a 10-year single center experience with parotid gland malignancies and to determine factors affecting outcomes. Study Design:Retrospective review. Methods:The institutional cancer registry was used to identify patients treated surgically for malignancies of the parotid gland between January 2005 and December 2014. Clinical and pathologic data were collected retrospectively from patient charts and analyzed for their association with overall survival (OS) and disease-free survival (DFS). Results:Two hundred patients were identified. Mean age at surgery was 57.8 years, and mean follow-up time was 52 months. One hundred two patients underwent total parotidectomy, while 77 underwent superficial parotidectomy, and 21 underwent deep lobe resection. Seventy patients (35%) required facial nerve (FN) sacrifice. Acinic cell carcinoma was the most common histologic type (22%), followed by mucoepidermoid carcinoma (21.5%) and adenoid cystic carcinoma (12.5%). Twenty-nine patients (14.5%) experienced recurrences, with mean time to recurrence of 23.6 months (range: 1-82 months). Five- and 10-year OS were 81% and 73%, respectively. Five- and 10-year DFS were 80% and 73%, respectively. In univariate analyses, age > 60, histologic type, positive margins, high grade, T-stage, node positivity, perineural invasion, and FN involvement were predictors of OS and DFS. In the multivariate analysis, histology, positive margins, node positivity, and FN involvement were independent predictors of OS and DFS. Conclusions:Our single-center experience of 200 patients suggests that histology, positive margins, node positivity, and FN involvement are independently associated with outcomes in parotid malignancies. Level of Evidence:4

Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis

BACKGROUND: Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. METHODS: We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. RESULTS: We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67-82]), than encephalopathy (54% [42-65]). Intensive care use was high (38% [35-41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27-32]. The hazard of death was comparatively lower for patients in the WHO European region. INTERPRETATION: Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission

Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis.

BackgroundNeurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome.MethodsWe conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models.ResultsWe included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67-82]), than encephalopathy (54% [42-65]). Intensive care use was high (38% [35-41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27-32]. The hazard of death was comparatively lower for patients in the WHO European region.InterpretationNeurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission

A 28-year-old male patient with asymptomatic and multi-drug-resistant HBV infection: a case report

Abstract Chronic hepatitis B virus (HBV) infection poses a significant global health challenge, impacting millions of individuals and elevating the risk of morbidity and mortality. Antiviral therapies are the primary treatment for chronic HBV infection, but treatment resistance can occur, leading to poor clinical outcomes and an increased risk of liver complications. This case report presents the clinical trajectory of a 28-year-old male diagnosed with asymptomatic HBV infection in 2016 under the auspices of the Hepatitis Control Program, Government of Azad Jammu and Kashmir, Pakistan. Over 6 years, persistent HBsAg, HBV, and HBeAg were observed, with absent acute markers and co-infections. Initial HBV DNA viral load was 1 × 104 copies/mL in 2016, escalating despite entecavir and pegylated interferons therapy, indicating multi-drug resistance. Tenofovir therapy initially reduced viral load but later exacerbated it, reaching 1.86 × 106 copies/mL in 2022. Liver function abnormalities and lipid profile irregularities persisted. Urine examination consistently showed abnormalities. Pending HBV DNA sequencing results may offer insights into treatment resistance. This case underscores the need for an adaptive approach in managing chronic HBV infections within public health programs. Continuous monitoring, integration of virological and biochemical data, and a tailored treatment strategy are essential for optimizing outcomes in similar cases, stressing the importance of refining therapeutic approaches against chronic HBV infection

Performance analysis of machine learning classifiers for non-technical loss detection

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Impact of Feature Selection on Non-Technical Loss Detection

Over the years, many countries have faced huge financial deficits due to Non-Technical Loss (NTL) in power sector. There are many ways of attempting to illegal use of electricity like by-passing and reversing meters. There have been many attempts to bring down NTL using manual and automated techniques. Manual NTL detection is not proving fruitful as it incurs heavy costs and has a low hit ratio. Due to the shortcoming of manual NTL detection, automated detection of NTL using machine learning classifiers is gaining attention in the research community. The datasets containing NTL belong to the class imbalance domain where regular consumers (negative class) out weight the representation of irregular consumers (positive class). To identify the right number of representative records, many techniques are proposed but selecting the right features in deciding NTL is equally an important task where not much has been contributed to the literature. In this paper, we propose the Incremental Feature Selection (IFS) algorithm which first uses feature importance to identify the most relevant features for NTL detection and then these features are used to test three classifiers namely CatBoost, Decision Tree (DT) Classifier and K-Nearest Neighbors (KNN) for NTL detection. This way, we have not only identified the most relevant features for NTL detection in a real dataset but also have brought down the overall computation time of the classifiers. Moreover, our proposed framework is tested on three performance evaluation metrics used in imbalance domain. The results show that using the most relevant features identified by the IFS algorithm, the three classifiers have the same or slightly better efficiency as compared to using all features. © 2020 IEEE