Sympathetic Neuronal Activation Triggers Myeloid Progenitor Proliferation and Differentiation.

There is a growing body of research on the neural control of immunity and inflammation. However, it is not known whether the nervous system can regulate the production of inflammatory myeloid cells from hematopoietic progenitor cells in disease conditions. Myeloid cell numbers in diabetic patients were strongly correlated with plasma concentrations of norepinephrine, suggesting the role of sympathetic neuronal activation in myeloid cell production. The spleens of diabetic patients and mice contained higher numbers of tyrosine hydroxylase (TH)-expressing leukocytes that produced catecholamines. Granulocyte macrophage progenitors (GMPs) expressed the ß2 adrenergic receptor, a target of catecholamines. Ablation of splenic sympathetic neuronal signaling using surgical, chemical, and genetic approaches diminished GMP proliferation and myeloid cell development. Finally, mice lacking TH-producing leukocytes had reduced GMP proliferation, resulting in diminished myelopoiesis. Taken together, our study demonstrates that catecholamines produced by leukocytes and sympathetic nerve termini promote GMP proliferation and myeloid cell development.

Does the implementation of revised American College of Cardiology and American Heart Association (ACC/AHA) guidelines improve the identification of stillbirths and preterm births in hypertensive pregnancies: a population-based cohort study from South Asia and sub-Saharan Africa.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a significant cause of maternal mortality worldwide. The classification and treatment of hypertension in pregnancy remain debated. We aim to compare the effectiveness of the revised 2017 ACC/AHA blood pressure threshold in predicting adverse pregnancy outcomes. METHODS: We conducted a secondary data analysis of the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, including 10,001 pregnant women from Bangladesh, Pakistan, and Tanzania. Blood pressure was measured using validated devices at different antenatal care visits. The blood pressure readings were categorized as: normal blood pressure (systolic blood pressure (sBP) < 120 mm Hg and diastolic blood pressure (dBP) < 80 mm Hg), elevated blood pressure (sBP 120-129 and dBP < 80), stage 1 hypertension (sBP 130-139 or dBP 80-89, or both), and stage 2 hypertension (sBP ≥ 140 or dBP ≥ 90, or both). We estimated risk ratios for stillbirths and preterm births, as well as diagnostic test properties of both the pre-existing JNC7 (≥ 140/90) and revised ACC/AHA (≥ 130/80) thresholds using normal blood pressure as reference group. RESULTS: From May 2014 to June 2018, blood pressure readings were available for 9,448 women (2,894 in Bangladesh, 2,303 in Pakistan, and 4,251 in Tanzania). We observed normal blood pressure in 70%, elevated blood pressure in 12.4%, stage 1 hypertension in 15.2%, and stage 2 hypertension in 2.5% of the pregnant women respectively. Out of these, 310 stillbirths and 9,109 live births were recorded, with 887 preterm births. Using the ACC/AHA criteria, the stage 1 hypertension cut-off revealed 15.3% additional hypertension diagnoses as compared to JNC7 criteria. ACC/AHA defined hypertension was significantly associated with stillbirths (RR 1.8, 95% CI 1.4, 2.3). The JNC 7 hypertension cut-off of ≥ 140/90 was significantly associated with a higher risk of preterm births (RR 1.6, 95% CI 1.2, 2.2) and stillbirths (RR 3.6, 95% CI 2.5, 5.3). Both criteria demonstrated low sensitivities (8.4 for JNC-7 and 28.1 for ACC/AHA) and positive predictive values (11.0 for JNC7 and 5.2 for ACC/AHA) in predicting adverse outcomes. CONCLUSION: The ACC/AHA criteria (≥ 130/80) identified additional cases of hypertension but had limited predictive accuracy for stillbirths and preterm births, highlighting the ongoing need for improved criteria in managing pregnancy-related hypertension.

Early to mid-pregnancy HbA1c levels and its association with adverse pregnancy outcomes in three low middle-income countries in Asia and Sub-Saharan Africa.

BACKGROUND: Hyperglycemia during pregnancy leads to adverse maternal and fetal outcomes. Thus, strict monitoring of blood glucose levels is warranted. This study aims to determine the association of early to mid-pregnancy HbA1c levels with the development of pregnancy complications in women from three countries in South Asia and Sub-Saharan Africa. METHODS: We performed a secondary analysis of the AMANHI (Alliance for Maternal and Newborn Health Improvement) cohort, which enrolled 10,001 pregnant women between May 2014 and June 2018 across Sylhet-Bangladesh, Karachi-Pakistan, and Pemba Island-Tanzania. HbA1c assays were performed at enrollment (8 to < 20 gestational weeks), and epidemiological data were collected during 2-3 monthly household visits. The women were followed-up till the postpartum period to determine the pregnancy outcomes. Multivariable logistic regression models assessed the association between elevated HbA1c levels and adverse events while controlling for potential confounders. RESULTS: A total of 9,510 pregnant women were included in the analysis. The mean HbA1c level at enrollment was found to be the highest in Bangladesh (5.31 ± 0.37), followed by Tanzania (5.22 ± 0.49) and then Pakistan (5.07 ± 0.58). We report 339 stillbirths and 9,039 live births. Among the live births were 892 preterm births, 892 deliveries via cesarean section, and 532 LGA babies. In the multivariate pooled analysis, maternal HbA1c levels of ≥ 6.5 were associated with increased risks of stillbirths (aRR = 6.3, 95% CI = 3.4,11.6); preterm births (aRR = 3.5, 95% CI = 1.8-6.7); and Large for Gestational Age (aRR = 5.5, 95% CI = 2.9-10.6). CONCLUSION: Maternal HbA1c level is an independent risk factor for predicting adverse pregnancy outcomes such as stillbirth, preterm birth, and LGA among women in South Asia and Sub-Saharan Africa. These groups may benefit from early interventional strategies.

Neurodevelopmental outcomes following possible serious bacterial infection in early infancy in Karachi, Pakistan: a prospective cohort study.

BACKGROUND: Pakistan reports a significant burden of neonatal mortality, with infections as one of the major causes. We aim to assess the long-term impact of early infancy infections on neurodevelopmental outcomes during later childhood. METHODS: We conducted a prospective follow-up study of the cohort enrolled at the Karachi site of the Aetiology of Neonatal Infection in South Asia (ANISA) during 2019-2020. Children with a possible serious bacterial infection (based on the WHO IMCI algorithm) at early infancy were assessed for neurodevelopment at 6-9 years of age and compared with healthy controls. The Ten Questions (TQS) questionnaire, Strengths and Difficulties Questionnaire (SDQ), and Parent's Evaluation of Developmental Stage Assessment Level (PEDS: DM-AL) neurodevelopmental assessment tools, were administered and scored by the research staff who were blinded to the child's exposure status. Generalized Structural Equation Modelling (GSEM) was employed to verify relationships and associations among developmental milestones, anthropometry, and sociodemographic variables. RESULTS: A total of 398 children (241 cases and 157 controls) completed neurodevelopmental and growth assessments. Cases had a significantly higher rate of abnormal TQS scores (54.5% vs. 35.0%, p-value 0.001), greater delays in motor milestones (21.2% vs. 12.1%, p-value 0.02), lower fine motor skills (78.4 ± 1.4 vs. 83.2 ± 1.5, p-value 0.02). The receptive language skills were well-developed in both groups. According to the logistic regression model, exposure to infection during the first 59 days of life was associated with delayed TQS milestones (ß = -0.6, 95% CI -1.2,-0.04), TQS hearing domain (ß = -0.3, 95% CI: -1.2 to 0.7), PEDS: DM-AL fine motor domain (ß = -1.3, 95% CI: -4.4 to 1.7), PEDS: DM-AL receptive language development (ß = -1.1, 95% CI: -3.7 to 1.4) and child anthropometric measurements such as weight and height (ß = -0.2, 95% CI: -0.4 to 0.01 and ß = -0.2, 95% CI: -0.4 to -0.01, respectively). Early pSBI exposure was positively associated with PEDS: DM-AL self-help domain (ß = 0.6, 95% CI: -1.2 to 2.4) and SDQ-P overall score (ß = 0.02, 95% CI: -0.3 to 0.3). CONCLUSION: Children exposed to PSBI during early infancy have higher rates of abnormal development, motor delays, and lower fine motor skills during later childhood in Pakistan. Socioeconomic challenges and limited healthcare access contribute to these challenges, highlighting the need for long-term follow-ups with integrated neurodevelopment assessments.

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network's security and integrity. This paper proposes a novel method-a Composite Ensemble Learning (CEL)-based neural network-for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

Sequential viral introductions and spread of BA.1 across Pakistan provinces during the Omicron wave.

BACKGROUND: COVID-19 waves caused by specific SARS-CoV-2 variants have occurred globally at different times. We focused on Omicron variants to understand the genomic diversity and phylogenetic relatedness of SARS-CoV-2 strains in various regions of Pakistan. METHODS: We studied 276,525 COVID-19 cases and 1,031 genomes sequenced from December 2021 to August 2022. Sequences were analyzed and visualized using phylogenetic trees. RESULTS: The highest case numbers and deaths were recorded in Sindh and Punjab, the most populous provinces in Pakistan. Omicron variants comprised 93% of all genomes, with BA.2 (32.6%) and BA.5 (38.4%) predominating. The first Omicron wave was associated with the sequential identification of BA.1 in Sindh, then Islamabad Capital Territory, Punjab, Khyber Pakhtunkhwa (KP), Azad Jammu Kashmir (AJK), Gilgit-Baltistan (GB) and Balochistan. Phylogenetic analysis revealed Sindh to be the source of BA.1 and BA.2 introductions into Punjab and Balochistan during early 2022. BA.4 was first introduced in AJK and BA.5 in Punjab. Most recent common ancestor (MRCA) analysis revealed relatedness between the earliest BA.1 genome from Sindh with Balochistan, AJK, Punjab and ICT, and that of first BA.1 from Punjab with strains from KPK and GB. CONCLUSIONS: Phylogenetic analysis provides insights into the introduction and transmission dynamics of the Omicron variant in Pakistan, identifying Sindh as a hotspot for viral dissemination. Such data linked with public health efforts can help limit surges of new infections.

Approaches to optimising access to NICE-approved biologic anti-TNFs for patients with rheumatoid arthritis with moderately active disease.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory disease that is associated with joint pain and stiffness. Biologics represent some of the most effective treatments for RA, but previous guidance from the National Institute for Health and Care Excellence (NICE) has limited their use to patients with severely active disease. This has meant patients with moderately active RA have been treated as if they have an acceptable disease state, despite many cases where the inflammation has a major impact on joint damage, mobility, pain and quality of life. However, recent guideline changes (NICE TA715) have approved the use of three biologics - adalimumab, etanercept and infliximab - for the treatment of moderately active RA. MAIN BODY: In response to these changes, we have held discussions with medical teams from across the UK to consider the main implications for implementation of these new recommendations, as well as any differences in approach that may exist at a local level. Several key challenges were identified. These included establishing methods of educating both physicians and patients concerning the new availability of the biologic treatments, with suggestions of various organisations that could be approached to circulate informative material. Identifying which patients with moderately active RA stand to benefit was another discussion topic. Relying solely on scoring systems like Disease Activity Score in 28 Joints (DAS28) was acknowledged to have limitations, and alternative complementary approaches such as ultrasound, as well as assessing a patient's co-morbidities, could also be useful tools in determining those who could benefit from biologics. An additional challenge for the process of patient identification has been the increase in the use of telemedicine consultations in response to the coronavirus disease 2019 (COVID-19) pandemic. More use of patient-reported outcomes was raised as one possible solution, and the importance of maintaining up-to-date databases on patient disease scores and treatment history was also stressed. CONCLUSION: While challenges exist in education and identifying patients who may benefit from the use of biologics, the NICE TA715 recommendations hold great potential in addressing an unmet need for the treatment of moderate RA.

Harmonization of maternal balanced energy-protein supplementation studies for individual participant data (IPD) meta-analyses - finding and creating similarities in variables and data collection.

BACKGROUND: Public health and clinical recommendations are established from systematic reviews and retrospective meta-analyses combining effect sizes, traditionally, from aggregate data and more recently, using individual participant data (IPD) of published studies. However, trials often have outcomes and other meta-data that are not defined and collected in a standardized way, making meta-analysis problematic. IPD meta-analysis can only partially fix the limitations of traditional, retrospective, aggregate meta-analysis; prospective meta-analysis further reduces the problems. METHODS: We developed an initiative including seven clinical intervention studies of balanced energy-protein (BEP) supplementation during pregnancy and/or lactation that are being conducted (or recently concluded) in Burkina Faso, Ethiopia, India, Nepal, and Pakistan to test the effect of BEP on infant and maternal outcomes. These studies were commissioned after an expert consultation that designed recommendations for a BEP product for use among pregnant and lactating women in low- and middle-income countries. The initiative goal is to harmonize variables across studies to facilitate IPD meta-analyses on closely aligned data, commonly called prospective meta-analysis. Our objective here is to describe the process of harmonizing variable definitions and prioritizing research questions. A two-day workshop of investigators, content experts, and advisors was held in February 2020 and harmonization activities continued thereafter. Efforts included a range of activities from examining protocols and data collection plans to discussing best practices within field constraints. Prior to harmonization, there were many similar outcomes and variables across studies, such as newborn anthropometry, gestational age, and stillbirth, however, definitions and protocols differed. As well, some measurements were being conducted in several but not all studies, such as food insecurity. Through the harmonization process, we came to consensus on important shared variables, particularly outcomes, added new measurements, and improved protocols across studies. DISCUSSION: We have fostered extensive communication between investigators from different studies, and importantly, created a large set of harmonized variable definitions within a prospective meta-analysis framework. We expect this initiative will improve reporting within each study in addition to providing opportunities for a series of IPD meta-analyses.

A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors.

Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in numerous applications. However, learning robust features requires an enormous number of labeled data. Therefore, implementing a DNN either requires creating a large dataset or needs to use the pre-trained models on different datasets. Multitask learning (MTL) is a machine learning paradigm where a model is trained to perform multiple tasks simultaneously, with the idea that sharing information between tasks can lead to improved performance on each individual task. This paper presents a novel MTL approach that employs combined training for human activities with different temporal scales of atomic and composite activities. Atomic activities are basic, indivisible actions that are readily identifiable and classifiable. Composite activities are complex actions that comprise a sequence or combination of atomic activities. The proposed MTL approach can help in addressing challenges related to recognizing and predicting both atomic and composite activities. It can also help in providing a solution to the data scarcity problem by simultaneously learning multiple related tasks so that knowledge from each task can be reused by the others. The proposed approach offers advantages like improved data efficiency, reduced overfitting due to shared representations, and fast learning through the use of auxiliary information. The proposed approach exploits the similarities and differences between multiple tasks so that these tasks can share the parameter structure, which improves model performance. The paper also figures out which tasks should be learned together and which tasks should be learned separately. If the tasks are properly selected, the shared structure of each task can help it learn more from other tasks.

Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation.

The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset-Sleep-EDFX-to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93-0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

Polarization-insensitive 1D unidirectional compact grating coupler for the C-band using a 500 nm SOI.

Grating couplers are an important optical interconnect and have increasingly found their utility in sensing and LIDARs as well. Optical systems in general have been struggling to increase their bandwidths, making polarization insensitivity highly desirable. The standard 220 nm silicon-on-insulator (SOI) platform used for integrated photonics suffers from physical bottlenecks in the form of large modal differences in effective refractive index, propagation loss, and dispersion. In this paper, we present a grating coupler for polarization-insensitive coupling with polarization-dependent loss of less than 0.2 dB for more than 80% of the C-band on an alternative 500 nm SOI platform. We further show that the same design can be extended to polarization inflexible coupling and can reduce the polarization-dependent loss to less than 0.08 dB for the complete C-band. This platform is devoid of shortcomings, making it better suited for polarization-insensitive photonics, and the coupler is able to achieve these results through a simple and compact 1D design.

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors.

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP).

Cyanobacteria derived compounds: Emerging drugs for cancer management.

The wide diversity of cyanobacterial species and their role in a variety of biological activities have been reported in the previous few years. Cyanobacteria, especially from marine sources, constitutes a major source of biologically active metabolites that have gained great attention especially due to their anticancer potential. Numerous chemically diverse metabolites from various cyanobacterial species have been recognized to inhibit the growth and progression of tumor cells through the induction of apoptosis in many different types of cancers. These metabolites activate the apoptosis in the cancer cells by different molecular mechanisms, however, the dysregulation of the mitochondrial pathway, death receptors signaling pathways, and the activation of several caspases are the crucial mechanisms that got considerable interest. The array of metabolites and the range of mechanisms involved may also help to overcome the resistance acquired by the different tumor types against the ongoing therapeutic agents. Therefore, the primary or secondary metabolites from the cyanobacteria as well as their synthetic derivates could be used to develop novel anticancer drugs alone or in combination with other chemotherapeutic agents. In this study, we have discussed the role of cyanobacterial metabolites in the induction of cytotoxicity and the potential to inhibit the growth of cancer cells through the induction of apoptosis, cell signaling alteration, oxidative damage, and mitochondrial dysfunctions. Moreover, the various metabolites produced by cyanobacteria have been summarized with their anticancer mechanisms. Furthermore, the ongoing trials and future developments for the therapeutic implications of these compounds in cancer therapy have been discussed.

Integrating Pharmacological and Computational Approaches for the Phytochemical Analysis of Syzygium cumini and Its Anti-Diabetic Potential.

Diabetes mellitus (DM) is a metabolic disease caused by improper insulin secretion leading to hyperglycemia. Syzygium cumini has excellent therapeutic properties due to its high levels of phytochemicals. The current research aimed to evaluate the anti-diabetic potential of S. cumini plant's seeds and the top two phytochemicals (kaempferol and gallic acid) were selected for further analysis. These phytochemicals were selected via computational tools and evaluated for α-Glucosidase inhibitory activity via enzymatic assay. Gallic acid (IC50 0.37 µM) and kaempferol (IC50 0.87 µM) have shown a stronger α-glucosidase inhibitory capacity than acarbose (5.26 µM). In addition, these phytochemicals demonstrated the highest binding energy, hydrogen bonding, protein-ligand interaction and the best MD simulation results at 100 ns compared to acarbose. Furthermore, the ADMET properties of gallic acid and kaempferol also fulfilled the safety criteria. Thus, it was concluded that S. cumini could potentially be used to treat DM. The potential bioactive molecules identified in this study (kaempferol and gallic acid) may be used as lead drugs against diabetes.

Direct maternal morbidity and the risk of pregnancy-related deaths, stillbirths, and neonatal deaths in South Asia and sub-Saharan Africa: A population-based prospective cohort study in 8 countries.

BACKGROUND: Maternal morbidity occurs several times more frequently than mortality, yet data on morbidity burden and its effect on maternal, foetal, and newborn outcomes are limited in low- and middle-income countries. We aimed to generate prospective, reliable population-based data on the burden of major direct maternal morbidities in the antenatal, intrapartum, and postnatal periods and its association with maternal, foetal, and neonatal death in South Asia and sub-Saharan Africa. METHODS AND FINDINGS: This is a prospective cohort study, conducted in 9 research sites in 8 countries of South Asia and sub-Saharan Africa. We conducted population-based surveillance of women of reproductive age (15 to 49 years) to identify pregnancies. Pregnant women who gave consent were include in the study and followed up to birth and 42 days postpartum from 2012 to 2015. We used standard operating procedures, data collection tools, and training to harmonise study implementation across sites. Three home visits during pregnancy and 2 home visits after birth were conducted to collect maternal morbidity information and maternal, foetal, and newborn outcomes. We measured blood pressure and proteinuria to define hypertensive disorders of pregnancy and woman's self-report to identify obstetric haemorrhage, pregnancy-related infection, and prolonged or obstructed labour. Enrolled women whose pregnancy lasted at least 28 weeks or those who died during pregnancy were included in the analysis. We used meta-analysis to combine site-specific estimates of burden, and regression analysis combining all data from all sites to examine associations between the maternal morbidities and adverse outcomes. Among approximately 735,000 women of reproductive age in the study population, and 133,238 pregnancies during the study period, only 1.6% refused consent. Of these, 114,927 pregnancies had morbidity data collected at least once in both antenatal and in postnatal period, and 114,050 of them were included in the analysis. Overall, 32.7% of included pregnancies had at least one major direct maternal morbidity; South Asia had almost double the burden compared to sub-Saharan Africa (43.9%, 95% CI 27.8% to 60.0% in South Asia; 23.7%, 95% CI 19.8% to 27.6% in sub-Saharan Africa). Antepartum haemorrhage was reported in 2.2% (95% CI 1.5% to 2.9%) pregnancies and severe postpartum in 1.7% (95% CI 1.2% to 2.2%) pregnancies. Preeclampsia or eclampsia was reported in 1.4% (95% CI 0.9% to 2.0%) pregnancies, and gestational hypertension alone was reported in 7.4% (95% CI 4.6% to 10.1%) pregnancies. Prolonged or obstructed labour was reported in about 11.1% (95% CI 5.4% to 16.8%) pregnancies. Clinical features of late third trimester antepartum infection were present in 9.1% (95% CI 5.6% to 12.6%) pregnancies and those of postpartum infection in 8.6% (95% CI 4.4% to 12.8%) pregnancies. There were 187 pregnancy-related deaths per 100,000 births, 27 stillbirths per 1,000 births, and 28 neonatal deaths per 1,000 live births with variation by country and region. Direct maternal morbidities were associated with each of these outcomes. CONCLUSIONS: Our findings imply that health programmes in sub-Saharan Africa and South Asia must intensify their efforts to identify and treat maternal morbidities, which affected about one-third of all pregnancies and to prevent associated maternal and neonatal deaths and stillbirths. TRIAL REGISTRATION: The study is not a clinical trial.

Hybrid dual-gain tunable integrated InP-Si3N4 external cavity laser.

We present a hybrid dual-gain integrated external cavity laser with full C-band wavelength tunability. Two parallel reflective semiconductor optical amplifier gain channels are combined by a Y-branch in the Si3N4 photonic circuit to increase the optical gain. A Vernier ring filter is integrated in the Si3N4 photonic circuit to select a single longitudinal mode and meanwhile reduce the laser linewidth. The side-mode suppression ratio is ∼67 dB with a pump current of 75 mA. The linewidth of the unpackaged laser is 6.6 kHz under on-chip output power of 23.5 mW. The dual-gain operation of the laser gives higher output power and narrower linewidth compared to the single gain operation. It is promising for applications in optical communications and light detection and ranging systems.

Insect-fungal-interactions: A detailed review on entomopathogenic fungi pathogenicity to combat insect pests.

Global food security is threatened by insect pests of economically important crops. Chemical pesticides have been used frequently for the last few decades to manage insect pests throughout the world. However, these chemicals are hazardous for human health as well as the ecosystem. In addition, several pests have evolved resistance to many chemicals. Finding environment friendly alternatives lead the researchers to introduce biocontrol agents such as entomopathogenic fungi (EPF). These fungi include various genera that can infect and kill insects efficiently. Moreover, EPFs have considerable host specificity with a mild effect on non-target organisms and can be produced in bulk quantity quickly. However, insights into the biology of EPF and mechanism of action are of prime significance for their efficient utilization as a biocontrol agent. This review focuses on EPF-mediated insect management by explaining particular EPF strains and their general mode of action. We have comprehensively discussed which criteria should be used for the selection of pertinent EPF, and which aspects can impact the EPF efficiency. Finally, we have outlined various advantages of EPF and their limitations. The article summarizes the prospects related to EPF utilization as biocontrol agents. We hope that future strategies for the management of insects will be safer for our planet.

Phase change material enabled 2 × 2 silicon nonvolatile optical switch.

Recently, large-scale photonic integrated circuits have seen rapid development. Optical switches are the elementary units used to realize optical routers and processors. However, the high static power and large footprint of silicon electro-optic and thermo-optic switches are becoming an obstacle for further scaling and high-density integration. In this Letter, we demonstrate a 2×2 nonvolatile silicon Mach-Zehnder optical switch enabled by low-loss phase change material Sb2S3. Changing the phase state of Sb2S3 can switch the optical transmission between the bar and cross paths. As no static power is required to maintain the phase state, it can find promising applications in optical switch matrices and reconfigurable optical circuits.