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

Clival chordoma : case report and review of recent developments in surgical and adjuvant treatments

Chordomas are rare tumors that can develop anywhere along the craniospinal axis. These tumors present challenges with respect to diagnosis and treatment due to a high rate of recurrence, even after multiple surgeries, and the propensity to involve any region within the craniospinal axis. New developments in radiation therapy have improved recurrence-free survival in patients with chordomas. Different regimens of chemotherapy and molecularly-targeted therapies, as adjuvants to surgery, have been described in individual case reports and case series. The purpose of this paper is to describe a case of clival chordoma and review recent developments in diagnostic and therapeutic options. A 77-year-old female was referred because of diplopia and progressively worsening headaches. Head imaging revealed a large expansile and erosive mass in the skull base. The patient underwent a successful endoscopic endonasal trans-sphenoidal resection of the mass, with biopsy confirming the diagnosis of chordoma. Postoperatively, the patient experienced an improvement in neurological symptoms. Chordomas can present a diagnostic challenge due to the rare occurrence and a tendency to involve any region within the craniospinal axis

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

A comprehensive multimodal humanoid system for personality assessment based on the Big Five model

Personality analysis allows the experts to get insights into an individual's conduct, vulnerabilities, and prospective capabilities. Some common methods employed for personality prediction include text analysis, social media data, facial expressions, and emotional speech extraction. Recently, some studies have utilized the big five model to predict personality traits using non-verbal cues (gaze score, body motion, head motion). However, these studies mostly target only three aspects of the big five mode. None of the studies so far have used non-verbal cues to target all five traits (extraversion, openness, neuroticism, agreeableness, and conscientiousness) of the Big Five model. In this paper, we propose a multi-modal system that predicts all five personality traits of the Big Five model using non-verbal cues (facial expressions, head poses, body poses), 44-item Big Five Inventory (BFI) questionnaire, and expert analysis. The facial expression module utilizes the Face Emotion Recognition Plus (FER+) dataset trained with Convolution Neural Network (CNN) model achieving 95.14% accuracy. Evaluating 16 subjects in verbal interaction with humanoid robot NAO, we combined questionnaire feedback, human-robot interaction data, and expert perspectives to deduce their Big Five traits. Findings reveal 100% accuracy in personality prediction via expert insights and the system, and 75% for the questionnaire-based approach

Midwife-led birthing centres in four countries: a case study.

BACKGROUND: Midwives are essential providers of primary health care and can play a major role in the provision of health care that can save lives and improve sexual, reproductive, maternal, newborn and adolescent health outcomes. One way for midwives to deliver care is through midwife-led birth centres (MLBCs). Most of the evidence on MLBCs is from high-income countries but the opportunity for impact of MLBCs in low- and middle-income countries (LMICs) could be significant as this is where most maternal and newborn deaths occur. The aim of this study is to explore MLBCs in four low-to-middle income countries, specifically to understand what is needed for a successful MLBC. METHODS: A descriptive case study design was employed in 4 sites in each of four countries: Bangladesh, Pakistan, South Africa and Uganda. We used an Appreciative Inquiry approach, informed by a network of care framework. Key informant interviews were conducted with 77 MLBC clients and 33 health service leaders and senior policymakers. Fifteen focus group discussions were used to collect data from 100 midwives and other MLBC staff. RESULTS: Key enablers to a successful MLBC were: (i) having an effective financing model (ii) providing quality midwifery care that is recognised by the community (iii) having interdisciplinary and interfacility collaboration, coordination and functional referral systems, and (iv) ensuring supportive and enabling leadership and governance at all levels. CONCLUSION: The findings of this study have significant implications for improving maternal and neonatal health outcomes, strengthening healthcare systems, and promoting the role of midwives in LMICs. Understanding factors for success can contribute to inform policies and decision making as well as design tailored maternal and newborn health programmes that can more effectively support midwives and respond to population needs. At an international level, it can contribute to shape guidelines and strengthen the midwifery profession in different settings

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