Ehf controls mammary alveolar lineage differentiation and is a putative suppressor of breast tumorigenesis

The transcription factor EHF is highly expressed in the lactating mammary gland, but its role in mammary development and tumorigenesis is not fully understood. Utilizing a mouse model of Ehf deletion, herein, we demonstrate that loss of Ehf impairs mammary lobuloalveolar differentiation at late pregnancy, indicated by significantly reduced levels of milk genes and milk lipids, fewer differentiated alveolar cells, and an accumulation of alveolar progenitor cells. Further, deletion of Ehf increased proliferative capacity and attenuated prolactin-induced alveolar differentiation in mammary organoids. Ehf deletion also increased tumor incidence in the MMTV-PyMT mammary tumor model and increased the proliferative capacity of mammary tumor organoids, while low EHF expression was associated with higher tumor grade and poorer outcome in luminal A and basal human breast cancers. Collectively, these findings establish EHF as a non-redundant regulator of mammary alveolar differentiation and a putative suppressor of mammary tumorigenesis.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion

BACKGROUND

Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot.

OBJECTIVE

In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other dataset.

METHODS

In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated.

RESULTS

92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy.

CONCLUSION

The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement for TCIA and BRaTS datasets respectively.

A lipid atlas of human and mouse immune cells provides insights into ferroptosis susceptibility

The cellular lipidome comprises thousands of unique lipid species. Here, using mass spectrometry-based targeted lipidomics, we characterize the lipid landscape of human and mouse immune cells ( www.cellularlipidatlas.com ). Using this resource, we show that immune cells have unique lipidomic signatures and that processes such as activation, maturation and development impact immune cell lipid composition. To demonstrate the potential of this resource to provide insights into immune cell biology, we determine how a cell-specific lipid trait-differences in the abundance of polyunsaturated fatty acid-containing glycerophospholipids (PUFA-PLs)-influences immune cell biology. First, we show that differences in PUFA-PL content underpin the differential susceptibility of immune cells to ferroptosis. Second, we show that low PUFA-PL content promotes resistance to ferroptosis in activated neutrophils. In summary, we show that the lipid landscape is a defining feature of immune cell identity and that cell-specific lipid phenotypes underpin aspects of immune cell physiology.

Search for Daily Modulation of MeV Dark Matter Signals with DAMIC-M

Dark matter (DM) particles with sufficiently large cross sections may scatter as they travel through Earth's bulk. The corresponding changes in the DM flux give rise to a characteristic daily modulation signal in detectors sensitive to DM-electron interactions. Here, we report results obtained from the first underground operation of the DAMIC-M prototype detector searching for such a signal from DM with MeV-scale mass. A model-independent analysis finds no modulation in the rate of 1  e^{-} events with sidereal period, where a DM signal would appear. We then use these data to place exclusion limits on DM in the mass range [0.53,2.7]  MeV/c^{2} interacting with electrons via a dark photon mediator. Taking advantage of the time-dependent signal we improve by ∼2 orders of magnitude on our previous limit obtained from the total rate of 1  e^{-} events, using the same dataset. This daily modulation search represents the current strongest limit on DM-electron scattering via ultralight mediators for DM masses around 1  MeV/c^{2}.

Evidence of a Hardening in the Cosmic Ray Proton Spectrum at around 166 TeV Observed by the GRAPES-3 Experiment

We present the measurement of the cosmic ray proton spectrum from 50 TeV to 1.3 PeV using 7.81×10^{6} extensive air shower events recorded by the ground-based GRAPES-3 experiment between 1 January 2014 and 26 October 2015 with a live time of 460 day. Our measurements provide an overlap with direct observations by satellite and balloon-based experiments. The electromagnetic and muon components in the shower were measured by a dense array of plastic scintillator detectors and a tracking muon telescope, respectively. The relative composition of the proton primary from the air shower data containing all primary particles was extracted using the multiplicity distribution of muons which is a sensitive observable for mass composition. The observed proton spectrum suggests a spectral hardening at ∼166  TeV and disfavors a single power law description of the spectrum up to the Knee energy (∼3  PeV).

Socioeconomic inequality among smoking and smokeless tobacco uses among males in India: a decomposition analysis

OBJECTIVE

India is the third-largest tobacco manufacturer and its use in India is characterised by a high prevalence of smoking and smokeless (sl) tobacco use. This results in 1 million deaths per year in the country. Given the high burden of tobacco use, this study examines the regional variations and socio-economic correlates of tobacco use in India.

METHODS

National Family Health Survey- 5 (2019-2020) have been analysed for the purpose of the study. A sample of 101,839 males aged 15-54 years was included in this study. Primary outcomes of tobacco use were categorised into smoking, smokeless and dual use of smoking and smokeless tobacco use. Bivariate analysis and decomposition analysis was done to study the socio-economic inequality.

RESULTS

The prevalence of tobacco use among males in India is around 41 percent. As indicated by the results of the logistics regression, age is positively related to smoking among males. Males aged 45-54 years are 2.5 (95 % concentration index [CI]:2.30-2.63) times probable to smoke, 1.4 (95% CI: 1.30-1.47) times probable of smokeless tobacco consumption and 2.2 (95% CI: 2.10-2.35) times more prone to using both types of substances compared to the younger age group. Males who are widower use smokeless 1.69 times (95% CI: 1.44-1.99) higher with reference to unmarried males. Males belonging to Scheduled tribes are 1.2 (95% CI: 1.13-1.25) times more likely to smoke, 1.3 (95% CI: 1.24-1.37) times more likely to use smokeless substances and 1.4 (95% CI: 1.33-1.47) times more likely to have dual use of tobacco than other social groups. Manual workers (both skilled are unskilled) are likely to smoke (odds ratio [OR] = 1.1, 95% CI: 1.02-1.11), use smokeless tobacco (OR = 1.3, 95% CI: 1.23-1.34) and have dual use of tobacco (OR = 1.29, 95% CI: 1.24-1.34) more than that of other categories. The decomposition of the concentration index shows a significant contribution from factors like a no education, ST/SC caste and wealth index. Among the states and union territories, the prevalence of tobacco is high in West Bengal, Tripura, Mizoram, Manipur, Meghalaya and Sikkim.

CONCLUSION

This study is useful for informing target-based prevention policies since it helps in highlighting regions, socio-economic and demographic groups especially vulnerable to tobacco addiction. In India, males from poorer and vulnerable socio-economic backgrounds are more likely to use tobacco. State wise, the eastern zone starting from West Bengal to the North-Eastern states have higher tobacco use than the rest of the country. There is an urgent need to frame policies for controlling the use of tobacco especially among high-risk groups.

Death in the Scottish curriculum: Denying or confronting?

The important role of schools in supporting children experiencing bereavement is established, yet less is known about how school curricula include death as part of life and this limits our understanding of the systemic structures that shape children's knowledge and experience of death. To address this gap, this paper discusses an analysis of the Scottish curriculum to explore the extent to which death features in compulsory education for children aged 3 to 15 years. The findings show that whilst death is present across the curricula, certain types of 'knowing' death are promoted, largely situated across religious teaching, which may limit children's engagement with the multiple and complex ways in which death features across individual, social, physical, and relational domains. By integrating the concepts of death systems and death ambivalence, the paper develops new knowledge on the interplay between curricula and sense making around death in children's lives that has practical utility.

Tests of Light-Lepton Universality in Angular Asymmetries of B^{0}→D^{*-}ℓν Decays

We present the first comprehensive tests of the universality of the light leptons in the angular distributions of semileptonic B^{0}-meson decays to charged spin-1 charmed mesons. We measure five angular-asymmetry observables as functions of the decay recoil that are sensitive to lepton-universality-violating contributions. We use events where one neutral B is fully reconstructed in ϒ(4S)→BB[over ¯] decays in data corresponding to 189  fb^{-1} integrated luminosity from electron-positron collisions collected with the Belle II detector. We find no significant deviation from the standard model expectations.

Precise Measurement of the D_{s}^{+} Lifetime at Belle II

We measure the lifetime of the D_{s}^{+} meson using a data sample of 207  fb^{-1} collected by the Belle II experiment running at the SuperKEKB asymmetric-energy e^{+}e^{-} collider. The lifetime is determined by fitting the decay-time distribution of a sample of 116×10^{3} D_{s}^{+}→ϕπ^{+} decays. Our result is τ_{D_{s}^{+}}=(499.5±1.7±0.9)  fs, where the first uncertainty is statistical and the second is systematic. This result is significantly more precise than previous measurements.

Search for a τ^{+}τ^{-} Resonance in e^{+}e^{-}→μ^{+}μ^{-}τ^{+}τ^{-} Events with the Belle II Experiment

We report the first search for a nonstandard-model resonance decaying into τ pairs in e^{+}e^{-}→μ^{+}μ^{-}τ^{+}τ^{-} events in the 3.6-10  GeV/c^{2} mass range. We use a 62.8  fb^{-1} sample of e^{+}e^{-} collisions collected at a center-of-mass energy of 10.58 GeV by the Belle II experiment at the SuperKEKB collider. The analysis probes three different models predicting a spin-1 particle coupling only to the heavier lepton families, a Higgs-like spin-0 particle that couples preferentially to charged leptons (leptophilic scalar), and an axionlike particle, respectively. We observe no evidence for a signal and set exclusion limits at 90% confidence level on the product of cross section and branching fraction into τ pairs, ranging from 0.7 to 24 fb, and on the couplings of these processes. We obtain world-leading constraints on the couplings for the leptophilic scalar model for masses above 6.5  GeV/c^{2} and for the axionlike particle model over the entire mass range.

Telehealth nursing interventions for phenotypes of older adults with COPD: an exploratory study

Introduction

Inconclusive results exist around the effectiveness of telemonitoring for patients with COPD, and studies recommended conducting subgroup analyses to identify patient phenotypes that could benefit from these services. This exploratory study investigated what type of COPD patients were receiving which type of telenursing interventions more frequently using the telemonitoring platform.

Methods

A sample of 36 older adults with COPD were receiving telenursing services for 12 months and were asked to answer five COPD-symptom related questions and submit their vital signs daily.

Results

Findings revealed two phenotypes of older adults for whom the frequency of telenursing calls and related interventions differed. Although no statistically significant differences were observed in participants' GOLD grades and hospitalizations, cluster one participants used their COPD action plan significantly more frequently, and were in frequent contact with the telenurse.

Discussion

It is paramount that further research is needed on the development of patient phenotypes who may benefit from telemonitoring.

Measurement of CP Violation in B^{0}→K_{S}^{0}π^{0} Decays at Belle II

We report a measurement of the CP-violating parameters C and S in B^{0}→K_{S}^{0}π^{0} decays at Belle II using a sample of 387×10^{6}  BB[over ¯] events recorded in e^{+}e^{-} collisions at a center-of-mass energy corresponding to the ϒ(4S) resonance. These parameters are determined by fitting the proper decay-time distribution of a sample of 415 signal events. We obtain C=-0.04_{-0.15}^{+0.14}±0.05 and S=0.75_{-0.23}^{+0.20}±0.04, where the first uncertainties are statistical and the second are systematic.

Defining the lipid profiles of human milk, infant formula, and animal milk: implications for infant feeding

Background

Breastfed infants have lower disease risk compared to formula-fed infants, however, the mechanisms behind this protection are unknown. Human milk has a complex lipidome which may have many critical roles in health and disease risk. However, human milk lipidomics is challenging, and research is still required to fully understand the lipidome and to interpret and translate findings. This study aimed to address key human milk lipidome knowledge gaps and discuss possible implications for early life health.

Methods

Human milk samples from two birth cohorts, the Barwon Infant Study (n = 312) and University of Western Australia birth cohort (n = 342), were analysed using four liquid chromatography-mass spectrometry (LC-MS) methods (lipidome, triacylglycerol, total fatty acid, alkylglycerol). Bovine, goat, and soy-based infant formula, and bovine and goat milk were analysed for comparison. Composition was explored as concentrations, relative abundance, and infant lipid intake. Statistical analyses included principal component analysis, mixed effects modelling, and correlation, with false discovery rate correction, to explore human milk lipidome longitudinal trends and inter and intra-individual variation, differences between sample types, lipid intakes, and correlations between infant plasma and human milk lipids.

Results

Lipidomics analysis identified 979 lipids. The human milk lipidome was distinct from that of infant formula and animal milk. Ether lipids were of particular interest, as they were significantly higher, in concentration and relative abundance, in human milk than in formula and animal milk, if present in the latter samples at all. Many ether lipids were highest in colostrum, and some changed significantly through lactation. Significant correlations were identified between human milk and infant circulating lipids (40% of which were ether lipids), and specific ether lipid intake by exclusively breastfed infants was 200-fold higher than that of an exclusively formula-fed infant.

Conclusion

There are marked differences between the lipidomes of human milk, infant formula, and animal milk, with notable distinctions between ether lipids that are reflected in the infant plasma lipidome. These findings have potential implications for early life health, and may reveal why breast and formula-fed infants are not afforded the same protections. Comprehensive lipidomics studies with outcomes are required to understand the impacts on infant health and tailor translation.

Test of Light-Lepton Universality in the Rates of Inclusive Semileptonic B-Meson Decays at Belle II

We present the first measurement of the ratio of branching fractions of inclusive semileptonic B-meson decays, R(X_{e/μ})=B(B→Xeν)/B(B→Xμν), a precision test of electron-muon universality, using data corresponding to 189  fb^{-1} from electron-positron collisions collected with the Belle II detector. In events where the partner B meson is fully reconstructed, we use fits to the lepton momentum spectra above 1.3  GeV/c to obtain R(X_{e/μ})=1.007±0.009(stat)±0.019(syst), which is the most precise lepton-universality test of its kind and agrees with the standard-model expectation.

First Measurement of the Michel Parameter ξ^{'} in the τ^{-}→μ^{-}ν[over ¯]_{μ}ν_{τ} Decay at Belle

We report the first measurement of the Michel parameter ξ^{'} in the τ^{-}→μ^{-}ν[over ¯]_{μ}ν_{τ} decay with a new method proposed just recently. The measurement is based on the reconstruction of the τ^{-}→μ^{-}ν[over ¯]_{μ}ν_{τ} events with subsequent muon decay in flight in the Belle central drift chamber. The analyzed data sample of 988  fb^{-1} collected by the Belle detector corresponds to approximately 912×10^{6} τ^{+}τ^{-} pairs. We measure ξ^{'}=0.22±0.94(stat)±0.42(syst), which is in agreement with the standard model prediction of ξ^{'}=1. Statistical uncertainty dominates in this study, being a limiting factor, while systematic uncertainty is well under control. Our analysis proved the practicability of this promising method and its prospects for further precise measurement in future experiments.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

Search for an Invisible Z^{'} in a Final State with Two Muons and Missing Energy at Belle II

The L_{μ}-L_{τ} extension of the standard model predicts the existence of a lepton-flavor-universality-violating Z^{'} boson that couples only to the heavier lepton families. We search for such a Z^{'} through its invisible decay in the process e^{+}e^{-}→μ^{+}μ^{-}Z^{'}. We use a sample of electron-positron collisions at a center-of-mass energy of 10.58 GeV collected by the Belle II experiment in 2019-2020, corresponding to an integrated luminosity of 79.7  fb^{-1}. We find no excess over the expected standard-model background. We set 90%-confidence-level upper limits on the cross section for this process as well as on the coupling of the model, which ranges from 3×10^{-3} at low Z^{'} masses to 1 at Z^{'} masses of 8  GeV/c^{2}.

First Measurement of the B^{+}→π^{+}π^{0}π^{0} Branching Fraction and CP Asymmetry

We study B^{+}→π^{+}π^{0}π^{0} using 711  fb^{-1} of data collected at the ϒ(4S) resonance with the Belle detector at the KEKB asymmetric-energy e^{+}e^{-} collider. We measure an inclusive branching fraction of (19.0±1.5±1.4)×10^{-6} and an inclusive CP asymmetry of (9.2±6.8±0.7)%, where the first uncertainties are statistical and the second are systematic, and a B^{+}→ρ(770)^{+}π^{0} branching fraction of (11.2±1.1±0.9_{-1.6}^{+0.8})×10^{-6}, where the third uncertainty is due to possible interference with B^{+}→ρ(1450)^{+}π^{0}. We present the first observation of a structure around 1  GeV/c^{2} in the π^{0}π^{0} mass spectrum, with a significance of 6.4σ, and measure a branching fraction to be (6.9±0.9±0.6)×10^{-6}. We also report a measurement of local CP asymmetry in this structure.

Search for Lepton-Flavor-Violating τ Decays to a Lepton and an Invisible Boson at Belle II

We search for lepton-flavor-violating τ^{-}→e^{-}α and τ^{-}→μ^{-}α decays, where α is an invisible spin-0 boson. The search uses electron-positron collisions at 10.58 GeV center-of-mass energy with an integrated luminosity of 62.8  fb^{-1}, produced by the SuperKEKB collider and collected with the Belle II detector. We search for an excess in the lepton-energy spectrum of the known τ^{-}→e^{-}ν[over ¯]_{e}ν_{τ} and τ^{-}→μ^{-}ν[over ¯]_{μ}ν_{τ} decays. We report 95% confidence-level upper limits on the branching-fraction ratio B(τ^{-}→e^{-}α)/B(τ^{-}→e^{-}ν[over ¯]_{e}ν_{τ}) in the range (1.1-9.7)×10^{-3} and on B(τ^{-}→μ^{-}α)/B(τ^{-}→μ^{-}ν[over ¯]_{μ}ν_{τ}) in the range (0.7-12.2)×10^{-3} for α masses between 0 and 1.6  GeV/c^{2}. These results provide the most stringent bounds on invisible boson production from τ decays.

First Constraints from DAMIC-M on Sub-GeV Dark-Matter Particles Interacting with Electrons

We report constraints on sub-GeV dark matter particles interacting with electrons from the first underground operation of DAMIC-M detectors. The search is performed with an integrated exposure of 85.23 g days, and exploits the subelectron charge resolution and low level of dark current of DAMIC-M charge-coupled devices (CCDs). Dark-matter-induced ionization signals above the detector dark current are searched for in CCD pixels with charge up to 7e^{-}. With this dataset we place limits on dark matter particles of mass between 0.53 and 1000  MeV/c^{2}, excluding unexplored regions of parameter space in the mass ranges [1.6,1000]  MeV/c^{2} and [1.5,15.1]  MeV/c^{2} for ultralight and heavy mediator interactions, respectively.

First Observation of Λπ^{+} and Λπ^{-} Signals near the K[over ¯]N(I=1) Mass Threshold in Λ_{c}^{+}→Λπ^{+}π^{+}π^{-} Decay

Using the data sample of 980  fb^{-1} collected with the Belle detector operating at the KEKB asymmetric-energy e^{+}e^{-} collider, we present the results of an investigation of the Λπ^{+} and Λπ^{-} invariant mass distributions looking for substructure in the decay Λ_{c}^{+}→Λπ^{+}π^{+}π^{-}. We find a significant signal in each mass distribution. When interpreted as resonances, we find for the Λπ^{+} (Λπ^{-}) combination a mass of 1434.3±0.6(stat)±0.9(syst)  MeV/c^{2} [1438.5±0.9(stat)±2.5(syst)  MeV/c^{2}], an intrinsic width of 11.5±2.8(stat)±5.3(syst)  MeV/c^{2} [33.0±7.5(stat)±23.6(syst)  MeV/c^{2}] with a significance of 7.5σ (6.2σ). As these two signals are very close to the K[over ¯]N threshold, we also investigate the possibility of a K[over ¯]N cusp, and find that we cannot discriminate between these two interpretations due to the limited size of the data sample.

IVIg therapy in the management of BK virus infections in pediatric kidney transplant patients

BK virus-associated nephropathy (BKPyVAN) induces kidney allograft dysfunction. Although decreasing immunosuppression is the standard for managing BK virus (BKPyV) infection, this strategy is not always effective. The use of polyvalent immunoglobulins (IVIg) may be of interest in this setting. We performed a retrospective single-center evaluation of the management of BKPyV infection in pediatric kidney transplant patients. Among the 171 patients who underwent transplantation between January 2010 and December 2019, 54 patients were excluded (combined transplant n = 15, follow-up in another center n = 35, early postoperative graft loss n= 4). Thus, 117 patients (120 transplants) were included. Overall, 34 (28%) and 15 (13%) transplant recipients displayed positive BKPyV viruria and viremia, respectively. Three had biopsy-confirmed BKPyVAN. The pre-transplant prevalence of CAKUT and HLA antibodies was higher among BKPyV-positive patients compared to non-infected patients. After the detection of BKPyV replication and/or BKPyVAN, the immunosuppressive regimen was modified in 13 (87%) patients: either by decreasing or changing the calcineurin inhibitors (n = 13) and/or switching from mycophenolate mofetil to mTor inhibitors (n = 10). Starting IVIg therapy was based on graft dysfunction or an increase in the viral load despite reduced immunosuppressive regimen. Seven of 15(46%) patients received IVIg. These patients had a higher viral load (5.4 [5.0-6.8]log vs. 3.5 [3.3-3.8]log). In total, 13 of 15 (86%) achieved viral load reduction, five of seven after IVIg therapy. As long as specific antivirals are not available for the management of BKPyV infections in pediatric kidney transplant patients, polyvalent IVIg may be discussed for the management of severe BKPyV viremia, in combination with decreased immunosuppression.

Observation of e^{+}e^{-}→ωχ_{bJ}(1P) and Search for X_{b}→ωϒ(1S) at sqrt[s] near 10.75 GeV

We study the processes e^{+}e^{-}→ωχ_{bJ}(1P) (J=0, 1, or 2) using samples at center-of-mass energies sqrt[s]=10.701, 10.745, and 10.805 GeV, corresponding to 1.6, 9.8, and 4.7  fb^{-1} of integrated luminosity, respectively. These data were collected with the Belle II detector during special operations of the SuperKEKB collider above the ϒ(4S) resonance. We report the first observation of ωχ_{bJ}(1P) signals at sqrt[s]=10.745  GeV. By combining Belle II data with Belle results at sqrt[s]=10.867  GeV, we find energy dependencies of the Born cross sections for e^{+}e^{-}→ωχ_{b1,b2}(1P) to be consistent with the shape of the ϒ(10753) state. These data indicate that the internal structures of the ϒ(10753) and ϒ(10860) states may differ. Including data at sqrt[s]=10.653  GeV, we also search for the bottomonium equivalent of the X(3872) state decaying into ωϒ(1S). No significant signal is observed for masses between 10.45 and 10.65  GeV/c^{2}.

Changes in sagittal and axial plane alignment in medial opening wedge high tibial osteotomy: a prospective observational study

PURPOSE

Deformity correction during high tibial osteotomy is generally based on coronal plane alignment. The aim of this study was to measure changes in sagittal and axial plane alignment following medial opening wedge high tibial osteotomy [mHTO], using pre- and postoperative computed tomography [CT] scans, and correlate them with changes in coronal plane. The secondary objective was to correlate changes in functional outcomes with changes in alignment in each plane. The null hypothesis was that sagittal and axial plane alignment does not change significantly following mHTO.

METHODS

This was a prospective, observational study including 30 patients of isolated medial compartment osteoarthritis of the knee joint, between 30 and 60 years of age. Preoperative and postoperative computed tomography scan of bilateral lower limbs from hip to ankle was performed. Varus angle, mechanical axis deviation [MAD] of the lower limb, medial proximal tibial angle [MPTA], tibial torsion and posterior proximal tibial angle [PPTA] were measured. Visual Analogue Scale [VAS 0-10 cm scale] was used for assessment of pain. Functional outcomes were evaluated using Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC], 12 Item Short Form Survey [SF 12] and Tegner Lysholm knee scoring scale.

RESULTS

Mean age of patients was 44.59 ± 8.157 years. Mean preoperative varus deformity was 11.13 ± 1.5 degrees, which got corrected to a mean valgus alignment of 3.8 ± 0.93 degrees postoperatively. The mechanical axis deviation was 4.32 ± 1.76 mm lateral to the knee joint center postoperatively. Preoperatively, all knees had external tibial torsion [27.08 ± 2.18 degrees] which was corrected to a mean external tibial torsion of 19.80 ± 3.72 degree after the surgery [P < 0.001]. The overall decrease in sagittal alignment in the present study was 3.70 ± 3.14 degree, 3.97 ± 3.06 degree, 3.92 ± 2.33 degree, for medial, middle and lateral cuts, respectively. There was a significant correlation between change in varus/valgus angle and change in TT and PPTA. Significant positive correlation was seen between change in coronal alignment and functional outcome scores.

CONCLUSION

This study has shown that mHTO significantly changes torsional and sagittal alignment. This is the first study to establish relation between correction in coronal plane and associated changes in sagittal and torsional alignments.

Search for a Dark Photon and an Invisible Dark Higgs Boson in μ^{+}μ^{-} and Missing Energy Final States with the Belle II Experiment

The dark photon A^{'} and the dark Higgs boson h^{'} are hypothetical particles predicted in many dark sector models. We search for the simultaneous production of A^{'} and h^{'} in the dark Higgsstrahlung process e^{+}e^{-}→A^{'}h^{'} with A^{'}→μ^{+}μ^{-} and h^{'} invisible in electron-positron collisions at a center-of-mass energy of 10.58 GeV in data collected by the Belle II experiment in 2019. With an integrated luminosity of 8.34  fb^{-1}, we observe no evidence for signal. We obtain exclusion limits at 90% Bayesian credibility in the range of 1.7-5.0 fb on the cross section and in the range of 1.7×10^{-8}-200×10^{-8} on the effective coupling ϵ^{2}×α_{D} for the A^{'} mass in the range of 4.0  GeV/c^{2}<M_{A^{'}}<9.7  GeV/c^{2} and for the h^{'} mass M_{h^{'}}<M_{A^{'}}, where ϵ is the mixing strength between the standard model and the dark photon and α_{D} is the coupling of the dark photon to the dark Higgs boson. Our limits are the first in this mass range.

Evidence of a New Excited Charmed Baryon Decaying to Σ_{c}(2455)^{0,++}π^{±}

We present the study of B[over ¯]^{0}→Σ_{c}(2455)^{0,++}π^{±}p[over ¯] decays based on 772×10^{6}  BB[over ¯] events collected with the Belle detector at the KEKB asymmetric-energy e^{+}e^{-} collider. The Σ_{c}(2455)^{0,++} candidates are reconstructed via their decay to Λ_{c}^{+}π^{∓} and Λ_{c}^{+} decays to pK^{-}π^{+}, pK_{S}^{0}, and Λπ^{+} final states. The corresponding branching fractions are measured to be B(B[over ¯]^{0}→Σ_{c}(2455)^{0}π^{+}p[over ¯])=(1.09±0.06±0.07)×10^{-4} and B(B[over ¯]^{0}→Σ_{c}(2455)^{++}π^{-}p[over ¯])=(1.84±0.11±0.12)×10^{-4}, which are consistent with the world average values with improved precision. A new structure is found in the M_{Σ_{c}(2455)^{0,++}π^{±}} spectrum with a significance of 4.2σ including systematic uncertainty. The structure is possibly an excited Λ_{c}^{+} and is tentatively named Λ_{c}(2910)^{+}. Its mass and width are measured to be (2913.8±5.6±3.8)  MeV/c^{2} and (51.8±20.0±18.8)  MeV, respectively. The products of branching fractions for the Λ_{c}(2910)^{+} are measured to be B(B[over ¯]^{0}→Λ_{c}(2910)^{+}p[over ¯])×B(Λ_{c}(2910)^{+}→Σ_{c}(2455)^{0}π^{+})=(9.5±3.6±1.6)×10^{-6} and B(B[over ¯]^{0}→Λ_{c}(2910)^{+}p[over ¯])×B(Λ_{c}(2910)^{+}→Σ_{c}(2455)^{++}π^{-})=(1.24±0.35±0.10)×10^{-5}. Here, the first and second uncertainties are statistical and systematic, respectively.

Micro and nano-scale compartments guide the structural transition of silk protein monomers into silk fibers

Silk is a unique, remarkably strong biomaterial made of simple protein building blocks. To date, no synthetic method has come close to reproducing the properties of natural silk, due to the complexity and insufficient understanding of the mechanism of the silk fiber formation. Here, we use a combination of bulk analytical techniques and nanoscale analytical methods, including nano-infrared spectroscopy coupled with atomic force microscopy, to probe the structural characteristics directly, transitions, and evolution of the associated mechanical properties of silk protein species corresponding to the supramolecular phase states inside the silkworm's silk gland. We found that the key step in silk-fiber production is the formation of nanoscale compartments that guide the structural transition of proteins from their native fold into crystalline β-sheets. Remarkably, this process is reversible. Such reversibility enables the remodeling of the final mechanical characteristics of silk materials. These results open a new route for tailoring silk processing for a wide range of new material formats by controlling the structural transitions and self-assembly of the silk protein's supramolecular phases.

Effect of Learning Parameters on the Performance of the U-Net Architecture for Cell Nuclei Segmentation from Microscopic Cell Images

Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesion, or overlapping of the nucleus. The automation process to find the cell’s nuclei is a giant leap in this direction and have an important step toward bioimage analysis employing software tools. This article extensively analyzes deep U-Net architecture and has been applied to the Data Science Bowl dataset to segment the cell nuclei. The dataset undergoes various pre-processing tasks such as resizing, intensity normalization, and data augmentation prior to segmentation. The complete dataset then undergoes rigorous training and validation process to find the optimized hyperparameters and then the optimized model selection. The mean (m) ± standard deviation (sd) of Intersection over Union (IOU), and F1-Score (Dice score) have been calculated along with Accuracy during the training and validation process, respectively. The optimized U-net model results in training IOU of 0.94 ± 0.16 (m ± s), F1-score of 0.94 ± 0.17 (m ± s), training accuracy of 95.54, and validation accuracy of 95.45. With that model, we have applied a completely independent test cohort of the dataset and obtained the mean IOU of 0.93, F1-score of 0.9311, and mean Accuracy of 94.12, respectively.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Search for Subsolar-Mass Binaries in the First Half of Advanced LIGO's and Advanced Virgo's Third Observing Run

We report on a search for compact binary coalescences where at least one binary component has a mass between 0.2  M_{⊙} and 1.0  M_{⊙} in Advanced LIGO and Advanced Virgo data collected between 1 April 2019 1500 UTC and 1 October 2019 1500 UTC. We extend our previous analyses in two main ways: we include data from the Virgo detector and we allow for more unequal mass systems, with mass ratio q≥0.1. We do not report any gravitational-wave candidates. The most significant trigger has a false alarm rate of 0.14  yr^{-1}. This implies an upper limit on the merger rate of subsolar binaries in the range [220-24200]  Gpc^{-3} yr^{-1}, depending on the chirp mass of the binary. We use this upper limit to derive astrophysical constraints on two phenomenological models that could produce subsolar-mass compact objects. One is an isotropic distribution of equal-mass primordial black holes. Using this model, we find that the fraction of dark matter in primordial black holes in the mass range 0.2  M_{⊙}<m_{PBH}<1.0  M_{⊙} is f_{PBH}≡Ω_{PBH}/Ω_{DM}≲6%. This improves existing constraints on primordial black hole abundance by a factor of ∼3. The other is a dissipative dark matter model, in which fermionic dark matter can collapse and form black holes. The upper limit on the fraction of dark matter black holes depends on the minimum mass of the black holes that can be formed: the most constraining result is obtained at M_{min}=1  M_{⊙}, where f_{DBH}≡Ω_{DBH}/Ω_{DM}≲0.003%. These are the first constraints placed on dissipative dark models by subsolar-mass analyses.

Alien genome mobilization and fixation utilizing an apomixis mediated genome addition (AMGA) strategy in Pennisetum to improve domestication traits of P. squamulatum

An approach to release 'frozen' variability in apomictic species using sexuality of another species, eventually its utilization in crop improvement and de-novo domestication of crop wild relatives is presented. Pennisetum squamulatum, a secondary gene pool species of pearl millet (P. glaucum), harbours many desirable traits. However, it was neither utilized to improve pearl millet fodder traits nor improvement of its own domestication traits was attempted, due to the complexities of genomes and apomictic reproduction. To overcome this, we followed an Apomixis Mediated Genome Addition (AMGA) strategy and utilized the contrasting reproductive capacities (sexuality and apomixis) of both the species to access the otherwise un-available variability embedded in P. squamulatum. Segregating population of interspecific hybrids exhibited significant variability and heterosis for desired morphological, agronomical, and nutritional traits. Elite apomictic and perennial hybrids were evaluated in breeding trials, and eventually a novel grass cultivar was released for commercial cultivation in India. The performance of newly developed cultivar was superior to other adapted perennial grasses of arid and semi-arid rangelands. Through AMGA, the sexuality of one species was successfully utilized to 'release' the 'frozen' variability embedded in another species. Subsequently, the hybrids representing desirable trait combinations were again 'fixed' utilizing the apomixis alleles from the male parent in a back-and-forth apomixis-sexual-apomixis selection cycle. This study also demonstrated the potential of AMGA to improve crop relatives through genomes introgression as well as de novo domestication of new crops from wild species.

Opioid withdrawal delirium without convulsions: A Rare Case report

Introduction

Opioid withdrawal symptoms classically include severe muscle cramps, bone aches, autonomic symptoms, anxiety. Patients seldom have other complications like delirium and convulsions unless they have comorbid medical illnesses.

Objectives

We hereby report a case of opioid withdrawal delirium.

Methods

A 20-year-old man with dependence for opiods and nicotine was admitted after compete history and mental status and physical examination, last intake for both substances 2 days back. There was no history of fever, head injury, siezures and other substance use. All investigations done were normal and urine drug screen was negative for other substances. Treatment was started with clonidine and quetiapine for sleep and Nsaids on prn basis. After 2 days there was hallucinatory behaviour, agitation, fleeting episodes of recognising family members, hearing voices and decreased sleep observed. Patient required sedation with 10 mg of lorazepam and haloperidol before he went to sleep.Later on lorazepam 8 mg in divided doses and clonidine was tapered off gradually and patient as discharged on naltrexone 50mg.

Results

In our case we could not find any other reason for delirium.These complications are rare feature of delirium, parker et all reported 5 such cases. One of limitations was we didnt do blood alcohol levels which could have ruled out alcohol use.

Conclusions

This case is unique in terms of presenting with delirium without convulsions after 4 days of abstinence. No associated comorbidities, organic causes, and other substance use in dependence pattern or recently used. Use of a street variety (mixed with impurities) could be a risk factor for delirium in our patient.Psychiatrist need to be aware of complication.

Disclosure

No significant relationships.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

Measurement of Two-Particle Correlations of Hadrons in e^{+}e^{-} Collisions at Belle

The measurement of two-particle angular correlation functions in high-multiplicity e^{+}e^{-} collisions at sqrt[s]=10.52  GeV is reported. In this study, the 89.5  fb^{-1} of hadronic e^{+}e^{-} annihilation data collected by the Belle detector at KEKB are used. Two-particle angular correlation functions are measured in the full relative azimuthal angle (Δϕ) and three units of pseudorapidity (Δη), defined by either the electron beam axis or the event-shape thrust axis, and are studied as a function of charged-particle multiplicity. The measurement in the thrust axis analysis, with mostly outgoing quark pairs determining the reference axis, is sensitive to the region of additional soft gluon emissions. No significant anisotropic collective behavior is observed with either coordinate analyses. Near-side jet correlations appear to be absent in the thrust axis analysis. The measurements are compared to predictions from various event generators and are expected to provide new constraints to the phenomenological models in the low-energy regime.

Measurement of the Nucleon F_{2}^{n}/F_{2}^{p} Structure Function Ratio by the Jefferson Lab MARATHON Tritium/Helium-3 Deep Inelastic Scattering Experiment

The ratio of the nucleon F_{2} structure functions, F_{2}^{n}/F_{2}^{p}, is determined by the MARATHON experiment from measurements of deep inelastic scattering of electrons from ^{3}H and ^{3}He nuclei. The experiment was performed in the Hall A Facility of Jefferson Lab using two high-resolution spectrometers for electron detection, and a cryogenic target system which included a low-activity tritium cell. The data analysis used a novel technique exploiting the mirror symmetry of the two nuclei, which essentially eliminates many theoretical uncertainties in the extraction of the ratio. The results, which cover the Bjorken scaling variable range 0.19<x<0.83, represent a significant improvement compared to previous SLAC and Jefferson Lab measurements for the ratio. They are compared to recent theoretical calculations and empirical determinations of the F_{2}^{n}/F_{2}^{p} ratio.

Serum Leptin Correlates with Obesity But Does Not Differ Between Gestational Diabetes and Normal Glucose Tolerance during 24-28 Weeks of Gestation

Leptin is an adipocytokine secreted by adipocytes which positively correlates with obesity. It is considered as a potential mediator for precipitating Gestational diabetes mellitus (GDM) which is more evident during 24-28 weeks of gestation. This study was conducted to see serum leptin level during 24-28 weeks of gestation in GDM at the Department of Endocrinology, BSMMU, Bangladesh from March 2019 to August 2020. Pregnant women (N=108) were challenged with 75gm oral glucose (OGTT) at 24-28 weeks of gestation and divided into GDM [n=45, age: 27.80±3.98 years, mean±SD; BMI: 27.88 (24.46-30.43) kg/m², median Interquartile range (IQR)] and normal glucose tolerance [NGT; n=62, age: 26.19±5.30 years, mean±SD; BMI: 25.80 (23.65-28.42) kg/m², median (IQR)] on basis of WHO-2013 diagnostic criteria. Fasting serum leptin and glucose were measured by ELISA and glucose oxidase method respectively. No statistically significant difference was found between GDM and NGT for leptin [26.05(16.92-50.55) vs. 23.50(14.95-38.30) median (IQR), p=0.360]. It was also not different statistically between GDM and NGT either for age groups (p=NS for all) or for Asian categories of BMI subgroups (p=NS for all). However, it was higher in subjects with BMI ≥23kg/m² than that with BMI ≤23kg/m² for both GDM [16.65 (6.39, 35.75) vs. 28.35 (19.60, 51.10) median (IQR), p=0.114] and NGT [14.65(9.19, 19.60) vs. 26.00 (17.30, 43.40) median (IQR), p=0.002]. It was also statistically similar in the GDM subgroups divided by Asian BMI cut-off (p=NS). BMI correlated with leptin in NGT (r=0.495, p<0.001) but not in GDM (r=0.177, p=0.251) and regression analysis revealed BMI (kg/m²) as predictor for high leptin (p=0.008). ROC curve analysis for leptin showed AUC for GDM was 0.553 (p=0.360) suggesting it as a poor predictor. It is concluded that fasting leptin in 24-28 weeks of gestation better relates with BMI but does not differ between GDM and NGT anddoes not seem to be a good predictor for GDM. Further study is required to make a comment on its prediction over GDM.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Citizen seismology helps decipher the 2021 Haiti earthquake

The August 14, Mw7.2, Nippes earthquake in Haiti occurred within the same fault zone as its devastating, Mw7.0, 2010 predecessor but struck the country when field access was limited by insecurity and conventional seismometers from the national network were inoperative. A network of citizen seismometers installed in 2019 provided near-field data critical to rapidly understand the mechanism of the mainshock and monitor its aftershock sequence. Their real-time data define two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. Machine learning applied to data from the citizen seismometer closest to the mainshock allows us to forecast aftershocks as accurately as with the network-derived catalog. This shows the utility of citizen science contributing to the understanding of a major earthquake.

Ether Lipids in Obesity: From Cells to Population Studies

Ether lipids are a unique class of glycero- and glycerophospho-lipid that carry an ether or vinyl ether linked fatty alcohol at the sn-1 position of the glycerol backbone. These specialised lipids are important endogenous anti-oxidants with additional roles in regulating membrane fluidity and dynamics, intracellular signalling, immunomodulation and cholesterol metabolism. Lipidomic profiling of human population cohorts has identified new associations between reduced circulatory plasmalogen levels, an abundant and biologically active sub-class of ether lipids, with obesity and body-mass index. These findings align with the growing body of work exploring novel roles for ether lipids within adipose tissue. In this regard, ether lipids have now been linked to facilitating lipid droplet formation, regulating thermogenesis and mediating beiging of white adipose tissue in early life. This review will assess recent findings in both population studies and studies using cell and animal models to delineate the functional and protective roles of ether lipids in the setting of obesity. We will also discuss the therapeutic potential of ether lipid supplementation to attenuate diet-induced obesity.

Ontogeny of circulating lipid metabolism in pregnancy and early childhood: a longitudinal population study

Background: There is mounting evidence that in utero and early life exposures may predispose an individual to metabolic disorders in later life; and dysregulation of lipid metabolism is critical in such outcomes. However, there is limited knowledge about lipid metabolism and factors causing lipid dysregulation in early life that could result in adverse health outcomes in later life. We studied the effect of antenatal factors such as gestational age, birth weight and mode of birth on lipid metabolism at birth; changes in the circulating lipidome in the first four years of life and the effect of breastfeeding in the first year of life. From this study, we aim to generate a framework for deeper understanding into factors effecting lipid metabolism in early life, to provide early interventions for those at risk of developing metabolic disorders including cardiovascular diseases.

Methods and findings: We performed comprehensive lipid profiling of 1074 mother-child dyads in the Barwon Infant Study (BIS), a population based pre-birth cohort and measured 776 distinct lipid species across 42 lipid classes using ultra high-performance liquid chromatography (UHPLC). We measured lipids in 1032 maternal serum samples at 28 weeks' gestation, 893 cord serum samples at birth, 793, 735, and 511 plasma samples at six, twelve months, and four years, respectively. The lipidome differed between mother and newborn and changed markedly with increasing child's age. Cord serum was enriched with long chain poly-unsaturated fatty acids (LC-PUFAs), and corresponding cholesteryl esters relative to the maternal serum. Alkenylphosphatidylethanolamine species containing LC-PUFAs increased with child's age, whereas the corresponding lysophospholipids and triglycerides decreased. We performed regression analyses to investigate the associations of cord serum lipid species with antenatal factors: gestational age, birth weight, mode of birth and duration of labor. Majority of the cord serum lipids were strongly associated with gestational age and birth weight, with most lipids showing opposing associations. Each mode of birth showed an independent association with cord serum lipids. Breastfeeding had a significant impact on the plasma lipidome in the first year of life, with upto 17-fold increases in a few species of alkyldiaclylglycerols at 6 months of age.

Conclusions: This study sheds light on lipid metabolism in infancy and early childhood and provide a framework to define the relationship between lipid metabolism and health outcomes in early childhood.

Funding Statement: This work was supported by the A*STAR-NHMRC joint call funding (1711624031).