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Abstract Background Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts. Methods This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint. Results In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63–4.66, p-value

Abstract Background Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. Methods A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. Results Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). Conclusions DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Introdução/objetivo: O surgimento de estirpes na tuberculose multirresistente e extensivamente resistente agravou os desafios terapêuticos e a resistência à drogas recém introduzidas, como a bedaquilina (BDQ). Assim, a análise estrutural in silico oferece uma abordagem promissora para identificar mutações que conferem resistência à BDQ. O presente estudo buscou analisar o impacto de polimorfismos na estrutura terciária das proteínas dos genes Rv0678 e mmpL5 em Mycobacterium tuberculosis (MTB) associadas à resistência à BDQ. Métodos: Foram utilizados dados de sequenciamento do Consórcio CRyPTIC para 3 cepas de MTB resistentes à BDQ. As leituras foram processadas com Trimmomatic, FastQC e MultiQC. O alinhamento e a chamada de polimorfismos de nucleotídeo único utilizaram BWA/SAMtools e MAGMA. As sequências foram traduzidas com EMBOSS Transeq. A modelagem e dinâmica molecular das variantes foram realizadas com SWISS-MODEL e subworkflow AlphaFold2 por meio do servidor web TBvar3D. Resultados: As proteínas analisadas obtiveram score global QMEANDisCo > 0.8 e estatísticas de Ramachandran > 90%, indicando confiança muito alta na predição. Em uma cepa, foi detectada variante de resistência em Rv0678 que apresentou a substituição de Ser por Arg na posição 63 e score PROVEAN (sP) de -4.98, o que está relacionado à mutação com perda de função em Rv0678, incidindo no aumento da concentração inibitória mínima da BDQ. Nas outras duas cepas, as mutações ocorreram em mmpL5, um componente de bomba de efluxo relacionada à exportação de sideróforos pela bactéria, sendo que em uma delas, foram encontradas duas variantes, com a substituição de Gly por Ser na posição 246 com sP de 1.33 e predição não associada à resistência, porém, a substituição de Asp por Asn na posição 767 revelou mutação de efeito deletério, com sP de -4.90. Já na outra cepa, houve substituição Ala por Thr na posição 359 com sP neutro de 1.14, e novamente, Asp por Asn na posição 767 com sP de -4.90. Conclusão: A análise in silico identifica mutações críticas como as com sP ≤-2.282 associadas à resistência à BDQ em MTB. As variantes de substituição detectadas nas cepas analisadas demonstraram impacto relevante na função proteica e resistência à droga. Tais achados destacam o valor das ferramentas de modelagem molecular com alta confiabilidade para prever e compreender mecanismos de resistência, o que em última análise fornecerá uma base sólida para novos alvos e estratégias terapêuticas. Palavras-chave: Tuberculose, Modelagem Computacional, Resistência a Drogas. Conflitos de interesse: Não houve conflitos de interesse. Ética e financiamentos: Financiamentos: IEC/SVSA/MS, CNPq e CAPES. Bolsa PIBIC/IEC, chamada 01/2023; Bolsa CAPES, chamada PPGBPA 120/2022; Bolsa DTI-C/CNPq, chamada 29/2023; Bolsa DTI-A/CNPq, chamada 29/2023; Bolsa Pq/CNPq, chamada 09/2022.

Abstract Aims This study aimed to evaluate the bidirectional relationship between kidney and cardiovascular (CV) events in trial participants with type 2 diabetes and CV disease. Methods and results Post hoc analyses of EMPA‐REG OUTCOME using Cox regression models were performed to assess the association of baseline factors with risk of a kidney event and bidirectional associations of incident kidney events and CV events. Among placebo‐treated participants, baseline factors significantly associated with greater kidney event risk included lower baseline estimated glomerular filtration rate, albuminuria, higher uric acid, low‐density lipoprotein cholesterol levels, and prior heart failure (HF). Coronary artery disease was not associated with increased risk. In placebo‐treated participants, occurrence of an incident non‐fatal kidney event increased the subsequent risk of hospitalization for HF (HHF) but not 3‐point major adverse CV events (non‐fatal stroke, non‐fatal myocardial infarction, and CV death). Vice versa, HHF (but not myocardial infarction/stroke) increased the risk of subsequent kidney events. These associations were generally also seen in empagliflozin‐treated participants and in the overall population. Interestingly, the risk of kidney events following HHF was not significantly increased in the relatively small number of placebo‐treated participants already diagnosed with HF at baseline. Conclusions These findings demonstrate a bidirectional inter‐relationship between HHF and kidney events. Further exploration of this relationship and strategies to optimize the use of therapies to reduce both kidney and HF outcomes is warranted.

The substantial increase in the human population dramatically strains food supplies. Farmers need healthy soil and natural minerals for traditional farming, and production takes a little longer time. The soil-free farming method known as vertical farming (VF) requires a small land and consumes a very small amount of water than conventional soil-dependent farming techniques. With modern technologies like hydroponics, aeroponics, and aquaponics, the notion of the VF appears to have a promising future in urban areas where farming land is very expensive and scarce. VF faces difficulty in the simultaneous monitoring of multiple indicators, nutrition advice, and plant diagnosis systems. However, these issues can be resolved by implementing current technical advancements like artificial intelligence (AI)-based control techniques such as machine learning (ML), deep learning (DL), the internet of things (IoT), image processing as well as computer vision. This article presents a thorough analysis of ML and IoT applications in VF system. The areas on which the attention is concentrated include disease detection, crop yield prediction, nutrition, and irrigation control management. In order to predict crop yield and crop diseases, the computer vision technique is investigated in view of the classification of distinct collections of crop images. This article also illustrates ML and IoT-based VF systems that can raise product quality and production over the long term. Assessment and evaluation of the knowledge-based VF system have also been outlined in the article with the potential outcomes, advantages, and limitations of ML and IoT in the VF system.

Abstract Metabolites are essential molecules involved in various metabolic processes, and their deficiencies and excessive concentrations can trigger significant physiological consequences. The detection of multiple metabolites within a non‐invasively collected biofluid could facilitate early prognosis and diagnosis of severe diseases. Here, a metal oxide heterojunction transistor (HJ‐TFT) sensor is developed for the label‐free, rapid detection of uric acid (UA) and 25(OH)Vitamin‐D3 (Vit‐D3) in human saliva. The HJ‐TFTs utilize a solution‐processed In2O3/ZnO channel functionalized with uricase enzyme and Vit‐D3 antibody for the selective detection of UA and Vit‐D3, respectively. The ultra‐thin tri‐channel architecture facilitates strong coupling between the electrons transported along the buried In2O3/ZnO heterointerface and the electrostatic perturbations caused by the interactions between the surface‐immobilized bioreceptors and target analytes. The biosensors can detect a wide range of concentrations of UA (from 500 nm to 1000 µM) and Vit‐D3 (from 100 pM to 120 nm) in human saliva within 60 s. Moreover, the biosensors exhibit good linearity with the physiological concentration of metabolites and limit of detections of ≈152 nm for UA and ≈7 pM for Vit‐D3 in real saliva. The specificity is demonstrated against various interfering species, including other metabolites and proteins found in saliva, further showcasing its capabilities.

Abstract Background Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. Methods This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. Results We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18–5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07–4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. Conclusion Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

This article presents a hybrid method to solve the selective harmonic elimination (SHE) problem for an adaptive DC bus multilevel inverter (MLI) topology. The proposed hybrid method combines the Grey wolf optimization (GWO) technique and Newton-Raphson (NR) numerical method which drastically minimizes the computational time and further improves the accuracy of the SHE solution. The technique is essentially divided into two stages. In the exploration stage, GWO explores the entire search space of SHE problem and provides initial values of switching angles for a given modulation index with a fast rate of exploration. In the exploitation stage, the NR method uses these initial switching angles as input to iteratively determine the optimum value of the firing angles. This GWO-inspired iterative methodology is implemented on a hybrid MLI topology for photovoltaic (PV) system applications. The performance of proposed SHE method is compared with recently developed metaheuristic SHE techniques based on some key performance indices. From an application point of view, the 11-Level MLI is tested on a PV battery-based stand-alone system to determine the effectiveness of the proposed method in MATLAB/Simulink environment. Finally, a small-scale experimental prototype is developed to validate the proposed method. The simulation and experimental results have been shown to demonstrate the effectiveness of GWO-NR SHE method in terms of excellent control over the targeted harmonics and fast dynamic control in the standalone PV system.

Odd-diffusive systems are characterized by transverse responses and exhibit unconventional behaviors in interacting systems. To address the dynamical interparticle rearrangements in a minimal system, we here exactly solve the problem of two hard disklike interacting odd-diffusing particles. We calculate the probability density function (PDF) of the interacting particles in the Fourier-Laplace domain and find that oddness rotates all modes except the zeroth, resembling a mutual rolling of interacting odd particles. We show that only the first Fourier mode of the PDF, the polarization, enters the calculation of the force autocorrelation function (FACF) for generic systems with central-force interactions. An analysis of the polarization as a function of time reveals that the relative rotation angle between interacting particles overshoots before relaxation, thereby rationalizing the recently observed oscillating FACF in odd-diffusive systems [Kalz et al., Phys. Rev. Lett. 132, 057102 (2024)0031-900710.1103/PhysRevLett.132.057102].

A previous study in Pará, Northern Brazil, described a strain of Mycobacterium tuberculosis with a unique genotype (SIT2517/T1) associated with multidrug-resistant tuberculosis (MDR-TB). To improve our understanding of MDR-TB transmission dynamics of these strains within this region, we performed phenotypic and genotypic drug susceptibility testing (pDST/gDST), 24-loci mycobacterial interspersed repetitive units (MIRU-VNTR) genotyping, whole-genome sequencing (WGS) and geo-epidemiology analysis. Of the 28 SIT2517/T1 isolates, 19 (67.9%) could be genotyped by 24-loci MIRU-VNTR and 15 by WGS. All belonged to sublineage 4.1.1.3, distinct from other representative Lineage 4 isolates identified in Brazil. The MDR phenotype determined by pDST was confirmed by gDST, the latter also demonstrating the presence of additional mutations conferring pre-extensively drug-resistance (pre-XDR). Discrepancies between gDST and pDST were observed for pyrazinamide and fluoroquinolones. Thirteen out of 15 isolates analyzed by WGS were clustered when applying a 12 single nucleotide polymorphisms (SNPs) cutoff. The SIT2517/T1 isolates were distributed across the metropolitan regions of Belém and Collares municipalities, showing no geographic clustering. WGS-transmission network analysis revealed a high likelihood of direct transmission and the formation of two closely linked transmission chains. This study highlights the need to implement TB genomic surveillance in the Brazilian Amazon region.

Abstract The effect of sodium-glucose co-transporter-2 (SGLT-2) inhibitors on cardiovascular and renal outcomes has not been systematically reviewed across baseline kidney function groups. We conducted a systematic review and meta-analysis of randomized control trials (RCTs) with SGLT-2 inhibitors in patients with and without CKD. We performed a PubMed/Medline search of randomized, placebo-controlled, event-driven outcome trials of SGLT-2 inhibitors versus active or placebo control in patients with and without diabetes from inception to November 2022. CKD was defined as an estimated glomerular filtration rate (eGFR)

Abstract The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.

Abstract One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell’s precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E−03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E−03 and 4.7571E−05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.

Background This study evaluated the effects of canagliflozin in patients with type 2 diabetes with and without prevalent cardiovascular disease (secondary and primary prevention). Methods and Results This was a pooled participant‐level analysis of the CANVAS (Canagliflozin Cardiovascular Assessment Study) Program and CREDENCE (Canagliflozin and Renal Events in Diabetes With Established Nephropathy Clinical Evaluation) trial. The CANVAS Program included participants with type 2 diabetes at elevated cardiovascular risk, whereas the CREDENCE trial included participants with type 2 diabetes and albuminuric chronic kidney disease. Hazard ratios (HRs) with interaction terms were obtained from Cox regression models to estimate relative risk reduction with canagliflozin versus placebo across the primary and secondary prevention groups. We analyzed 5616 (38.9%) and 8804 (61.1%) individuals in the primary and secondary prevention subgroups, respectively. Primary versus secondary prevention participants were on average younger (62.2 versus 63.8 years of age) and more often women (42% versus 31%). Canagliflozin reduced the risk of major adverse cardiovascular events (HR, 0.84 [95% CI, 0.76–0.94]) consistently across primary and secondary prevention subgroups (Pinteraction=0.86). Similarly, no treatment effect heterogeneity was observed with canagliflozin for hospitalization for heart failure, cardiovascular death, end‐stage kidney disease, or all‐cause mortality (all Pinteraction>0.5). Conclusions Canagliflozin reduced cardiovascular and kidney outcomes with no statistical evidence of heterogeneity for the treatment effect across the primary and secondary prevention subgroups in the CANVAS Program and CREDENCE trial. Although studies on the optimal implementation of canagliflozin within these populations are warranted, these results reinforce canagliflozin's role in cardiorenal prevention and treatment in individuals with type 2 diabetes. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT01032629, NCT01989754, NCT02065791.

Five mycobacterial isolates from sewage were classified as members of the genus Mycobacterium but presented inconclusive species assignments. Thus, the isolates (MYC017, MYC098, MYC101, MYC123 and MYC340) were analyzed by phenotypical, biochemical, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and genomic features to clarify their taxonomic position. Phenotypic analysis and biochemical tests did not distinguish these isolates from other non-pigmented mycobacteria. In contrast, MALDI-TOF MS analysis showed that isolates were not related to any previously described Mycobacterium species. Comparative genomic analysis showed values of ANI and dDDH between 81.59–85.56% and 24.4–28.8%, respectively, when compared to the genomes of species of this genus. In addition, two (MYC101 and MYC123) presented indistinguishable protein spectra from each other and values of ANI = 98.57% and dDDH = 97.3%, therefore being considered as belonging to the same species. Phylogenetic analysis grouped the five isolates within the Mycobacterium terrae complex (MTC) but in a specific subclade and separated from the species already described and supported by 100% bootstrap value, confirming that they are part of this complex but different from earlier described species. According to these data, we propose the description of four new species belonging to the Mycobacterium genus: (i) Mycobacterium defluvii sp. nov. strain MYC017T (= ATCC TSD-296T = JCM 35364T), (ii) Mycobacterium crassicus sp. nov. strain MYC098T (= ATCC TSD-297T = JCM 35365T), (iii) Mycobacterium zoologicum sp. nov. strain MYC101T (= ATCC TSD-298T = JCM 35366T) and MYC123 (= ATCC BAA-3216 = JCM 35367); and (iv) Mycobacterium nativiensis sp. nov. strain MYC340T (= ATCC TSD-299T = JCM 35368T).

The scope of the work presented in this paper is based on the principles of circular economy. Functional oleogels were prepared from pre-optimized supercritically extracted walnut oil (350 bar/40 °C/90 min) as a butter alternative that can meet more than 50% of the Required Dietary Allowance of ω-3 fatty acids per serving of 5.75 g. The spent residue of walnut kernel after supercritical extraction was incorporated in cookie formulations to replace wheat flour as a valorization approach. Carnauba and beeswax were used as oleogelator in the oleogels so prepared by optimizing the rheological and textural parameters of the oleogels. Prior to this development, the yield, fatty acid composition, tocopherol content, antioxidant activity as well as total polyphenolic content (TPC) was compared with conventional soxhlet using n-hexane for extraction. Cookies were formulated by replacing 15% w/w refined flour with the protein and fiber-rich spent residue with good overall acceptability (7) when compared to control (8) and textural properties like hardness (4125 g) and fracturability (8 mm). The oleogels could be vulnerable to oxidation during storage and hence their stability as well as the cookies needs further evaluation. Approaches to increase the same are also warranted. The approach so presented can have positive implications for both economy as well as the environment.

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

In multilevel inverters (MLI), output voltage waveform consists of dominant low order harmonics, which needs to be minimized. Simultaneously, good control over the fundamental voltage for desirable operation is needed. In this paper, an atom search optimization (ASO) based selective harmonic elimination (SHE) method is proposed for a variable dc bus based reduced component count (RCC) MLI. ASO is a population-based metaheuristic algorithm, which mathematically models the motion of atoms in nature to accurately determine the optimum firing angle of the switches by solving SHE fitness function. The proposed ASO SHE method outperforms recent metaheuristics based SHE methods such as bee algorithm, imperialistic colonial algorithm (ICA), firefly, particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) in solving SHE problem for 11-level multilevel inverter. Detailed simulation case studies are presented to effectively demonstrate the performance of the proposed ASO SHE method on a stand-alone photovoltaic (PV) based RCC MLI subjected to sudden changes in irradiance, load and dc-link capacitor voltage. The experimental results on a PV based variable dc bus multilevel inverter validate the excellent performance of ASO SHE method in minimizing the total harmonic distortion (THD) and dominant order harmonics under sudden change in operating conditions.

An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm’s novelty lies primarily in the improved search strategies, where two modifications are proposed to maintain a proper balance between exploration and exploitation. Firstly, an opposition-based learning mechanism is employed to initialize the search population for the purpose of enhancing the global search. Secondly, Lévy flight distribution is used to prevent the stagnation of solutions in local minima. The implementation of intelligent rules such as OBL and Lévy flight distribution significantly improves the performance of the standard MFO. The developed OBLVMFO performed adequately and is reliable in terms of RMSE compared to other methodologies such as MFO, ALO, SCA, MRFO, and WOA. The best optimized value of RMSE achieved by OBLVMFO is 6.060E−04, 1.3600E−05, and 7.0001E−06 for STE 4/100 (polycrystalline), LSM 20 (monocrystalline), and SS2018P (polycrystalline) PV modules, respectively. The experiments performed on the benchmark test function revealed that the OBLVMFO has a 61% faster convergence speed than the standard version of MFO, which improves solution accuracy. In addition to this, two non-parametric tests: Friedman ranking and Wilcoxon rank sum are performed for the validation.

Objectives COMMANDER-HF was a randomised trial comparing rivaroxaban 2.5 mg two times a day to placebo, in addition to antiplatelet therapy, in patients hospitalised for worsening heart failure with coronary artery disease and sinus rhythm. Patients with diabetes are at increased risk of cardiovascular events and therefore have more to gain.Methods and results In this post-hoc analysis, we evaluated the efficacy and safety of rivaroxaban in patients with (n=2052) and without diabetes (n=2970). The primary outcome was the composite of cardiovascular death, myocardial infarction (MI) or ischaemic stroke. HRs and 95% CIs with interaction analyses were used to describe event-rates and treatment effects. Patients with diabetes had a higher prevalence of cardiovascular comorbidities (eg, hypertension, obesity) and increased incidence of cardiovascular events. Adjusted HRs for events in people with versus without diabetes were 1.34 (95% CI 1.19 to 1.50) for the primary outcome, 1.21 (95% CI 0.84 to 1.75) for stroke, 1.51 (95% CI 1.14 to 1.99) for MI, 1.17 (95% CI 1.05 to 1.31) for heart failure hospitalisation and 1.06 (95% CI 0.56 to 2.01) for major bleeding. Rivaroxaban had no significant effect on event-rates in patients with and without diabetes (all interaction p values >0.05). Low-dose rivaroxaban was associated with an overall reduction in ischaemic stroke (HR 0.66; 95% CI 0.47 to 0.95), with no apparent subgroup interaction according to diabetes status (p-int=0.93).Conclusions In COMMANDER-HF a diagnosis of diabetes conferred higher rates of cardiovascular events that, with exception of ischaemic stroke, was not substantially reduced by rivaroxaban. Rivaroxaban was associated with reduced risk of ischaemic stroke for patients with and without diabetes.Trial registration number NCT01877915; Post-results.

Abstract We propose a mesoscopic Brownian magneto heat pump made of a single charged Brownian particle that is steered by an external magnetic field. The particle is subjected to two thermal noises from two different heat sources. When confined, the particle performs gyrating motion around a potential energy minimum. We show that such a magneto-gyrator can be operated as both a heat engine and a refrigerator. The maximum power delivered by the engine and the performance of the refrigerator, namely the rate of heat transferred per unit external work, can be tuned and optimised by the applied magnetic field. Further tunability of the key properties of the engine, such as the direction of gyration and the torque exerted by the engine on the confining potential, is obtained by varying the strength and direction of the applied magnetic field. In principle, our predictions can be tested by experiments with colloidal particles and complex plasmas.

Background Frailty is prevalent in older adults with heart failure and is associated with poor outcomes; however, there remains uncertainty on how to measure frailty in clinical practice. Methods and Results A multicentric prospective cohort study was assembled at 4 heart failure clinics to compare the prognostic value of 3 physical frailty scales in ambulatory patients with heart failure. Outcomes were all‐cause death or hospitalization and health‐related quality of life using the 36‐Item Short Form survey questionnaire (SF‐36) at 3 months. Multivariable regression was adjusted for age, sex, Meta‐Analysis Global Group in Chronic Heart Failure score, and baseline SF‐36 score. The cohort included 215 patients (mean age 77.6 years). All 3 frailty scales were independently associated with death or hospitalization at 3 months; the adjusted odds ratios standardized per 1 SD worsening of the Short Physical Performance Battery; Fried, and strength, assistance with walking, rising from a chair, climbing stairs, and falls scales were 1.67 (95% CI, 1.09–2.55), 1.60 (95% CI, 1.04–2.46), and 1.55 (95% CI, 1.03–2.35), respectively, with C statistics of 0.77 to 0.78. All 3 frailty scales were independently associated with worsening SF‐36 scores, especially the Short Physical Performance Battery, for which 1 SD worsening of frailty translated to a decrement of −5.86 (−8.55 to −3.17) and −5.51 (−7.82 to −3.21) points in the Physical Component Score and Mental Component Score. Conclusions All 3 physical frailty scales were associated with death, hospitalization, and reduced health‐related quality of life in ambulatory patients with heart failure. Questionnaire or performance‐based physical frailty scales can be used to offer prognostic value and a therapeutic target in this vulnerable population. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03887351.

Background: Biological effects of radiation include deterministic and stochastic effects. Dental colleges still follow radiation protection principles due to guidelines laid by the Dental Council of India and the Atomic Energy Regulatory Board, but private practitioners sometimes are negligent about the significance of radiation protection. The study focuses on the awareness of radiation protection among dental practitioners based on their knowledge, attitude, and application. Aim and Objectives: The aim and objectives of this study were to evaluate the level of awareness and attitude about radiation protection among dental practitioners in Uttar Pradesh and the NCR region, India. Materials and Methods: The survey was performed on dental practitioners from UP and NCR region, India. The survey comprised two sections: one section on basic information of the dentists and their working place, consisting of two questions. The second section consisted of 21 questions regarding patient and personnel protection methods and knowledge about radiation exposure. The questionnaire was disseminated through Google Forms. Results: Among 200 dentists, 84% use Radio visual graph (RVG) in their clinics. Ninety percent of the specialists attended the basic lecture on radiation exposure, nearly 50% of the dentists use digital receptors in pediatric patients, 86% of the specialists use their hands when adjusting the X-ray tube, 98.4% of the dentists know the related complications of radiation, 46.8% of the specialists explain the radiation measures to the patients, 76.2% of the dentists use lead barriers in their clinics, and 76.2% of the clinicians often get their X-ray machines serviced yearly. Conclusion: The current study shows the utmost need for further implementation of radiation protection. The majority of dentists are negligent in practicing radiation protection procedures properly. Emphasis on radiation safety and mandatory professional education is needed.

Objective: The objective of this study was to evaluate the short-term efficacy and safety of the topical application of amlexanox paste with triamcinolone acetonide paste and tacrolimus paste in the treatment of oral erosive lichen planus. Study Design: This study was a randomized double-blinded placebo-controlled clinical trial. Forty patients aged 18–70 years with erosive oral lichen planus received amlexanox 5% paste (n = 10), triamcinolone acetonide 0.1% paste (n = 10), tacrolimus 0.03% paste (n = 10), and glycerin as placebo (n = 10) for 15 days. Outcome measures included pain using visual analog scale (VAS) scores, and the erosive area in cm2 was evaluated both before (baseline) and during the trial (i.e., days 7 and 15). Results: After 15 days of treatment, all the groups showed significant reductions in erosive areas and VAS scores (P

BackgroundThe COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient care delivery. Digital tools can help support health care institutions by tracking transmission of the virus, aiding in the screening process, and providing telemedicine support. However, digital health tools face challenges associated with barriers to accessibility, efficiency, and privacy-related ethical issues. ObjectiveThis paper describes the study design of an open-label, noninterventional, crossover, randomized controlled trial aimed at assessing whether interactive voice response systems can screen for SARS-CoV-2 in patients as accurately as standard screening done by people. The study aims to assess the concordance and interrater reliability of symptom screening done by Amazon Alexa compared to manual screening done by research coordinators. The perceived level of comfort of patients when interacting with voice response systems and their personal experience will also be evaluated. MethodsA total of 52 patients visiting the heart failure clinic at the Royal Victoria Hospital of the McGill University Health Center, in Montreal, Quebec, will be recruited. Patients will be randomly assigned to first be screened for symptoms of SARS-CoV-2 either digitally, by Amazon Alexa, or manually, by the research coordinator. Participants will subsequently be crossed over and screened either digitally or manually. The clinical setup includes an Amazon Echo Show, a tablet, and an uninterrupted power supply mounted on a mobile cart. The primary end point will be the interrater reliability on the accuracy of randomized screening data performed by Amazon Alexa versus research coordinators. The secondary end point will be the perceived level of comfort and app engagement of patients as assessed using 5-point Likert scales and binary mode responses. ResultsData collection started in May 2021 and is expected to be completed in fall 2022. Data analysis is expected to be completed in early 2023. ConclusionsThe use of voice-based assistants could improve the provision of health services and reduce the burden on health care personnel. Demonstrating a high interrater reliability between Amazon Alexa and health care coordinators may serve future digital tools to streamline the screening and delivery of care in the context of other conditions and clinical settings. The COVID-19 pandemic occurs during the first digital era using digital tools such as Amazon Alexa for disease screening, and it represents an opportunity to implement such technology in health care institutions in the long term. Trial RegistrationClinicalTrials.gov NCT04508972; https://clinicaltrials.gov/ct2/show/NCT04508972 International Registered Report Identifier (IRRID)DERR1-10.2196/41209

IntroductionThe COVID-19 pandemic disrupted newborn care and breastfeeding practices across most healthcare facilities. We undertook this study to explore the barriers and enablers for newborn care and breastfeeding practices in hospitals in Delhi, India for recently delivered mother (RDM)–newborn dyads during the first wave of the COVID-19 pandemic (2020) and inductively design a “pathway of impaction” for informing mitigatory initiatives during the current and future pandemics, at least in the initial months.Materials and methodsWe used an exploratory descriptive design (qualitative research method) and collected information from seven leading public health facilities in Delhi, India. We conducted separate interviews with the head and senior faculty from the Departments of Pediatrics/Neonatology (n = 12) and Obstetrics (n = 7), resident doctors (n = 14), nurses (labor room/maternity ward; n = 13), and RDMs (n = 45) across three profiles: (a) COVID-19-negative RDM with healthy newborn (n = 18), (b) COVID-19-positive RDM with healthy newborn (n = 19), and (c) COVID-19 positive RDM with sick newborn needing intensive care (n = 8) along with their care-giving family members (n = 39). We analyzed the data using grounded theory as the method and phenomenology as the philosophy of our research.ResultsAnxiety among clients and providers, evolving evidence and advisories, separation of the COVID-positive RDM from her newborn at birth, providers' tendency to minimize contact duration and frequency with COVID-positive mothers, compromised counseling on breastfeeding, logistic difficulties in expression and transportation of COVID-positive mother's milk to her baby in the nursery, COVID restrictions, staff shortage and unavailable family support in wards and nursery, and inadequate infrastructure were identified as major barriers. Keeping the RDM–newborn together, harmonization of standard operating procedures between professional associations and within and between departments, strategic mobilization of resources, optimization of human resources, strengthening client–provider interaction, risk triaging, leveraging technology, and leadership-in-crisis-situations were notable enablers.ConclusionThe separation of the RDM and newborn led to a cascade of disruptions to newborn care and breastfeeding practices in the study institutions. Separating the newborn from the mother should be avoided during public health emergencies unless there is robust evidence favoring the same; routine institutional practices should be family centered.

The limited ability of cancer therapeutics in crossing the cancer cell membrane hampers their therapeutic potential. Here, the authors report Salmonella-based system for intracellular delivery of protein drugs, e.g. caspase-3, and show reduction of tumors in mouse models of breast and liver cancer.

Background Published randomized controlled trials are underpowered for binary clinical end points to assess the safety and efficacy of renin‐angiotensin system inhibitors (RASi) in adults with COVID‐19. We therefore performed a meta‐analysis to assess the safety and efficacy of RASi in adults with COVID‐19. Methods and Results MEDLINE, EMBASE, ClinicalTrials.gov, and the Cochrane Controlled Trial Register were searched for randomized controlled trials that randomly assigned patients with COVID‐19 to RASi continuation/commencement versus no RASi therapy. The primary outcome was all‐cause mortality at ≤30 days. A total of 14 randomized controlled trials met the inclusion criteria and enrolled 1838 participants (aged 59 years, 58% men, mean follow‐up 26 days). Of the trials, 11 contributed data. We found no effect of RASi versus control on all‐cause mortality (7.2% versus 7.5%; relative risk [RR], 0.95; [95% CI, 0.69–1.30]) either overall or in subgroups defined by COVID‐19 severity or trial type. Network meta‐analysis identified no difference between angiotensin‐converting enzyme inhibitors versus angiotensin II receptor blockers. RASi users had a nonsignificant reduction in acute myocardial infarction (2.1% versus 3.6%; RR, 0.59; [95% CI, 0.33–1.06]), but increased risk of acute kidney injury (7.0% versus 3.6%; RR, 1.82; [95% CI, 1.05–3.16]), in trials that initiated and continued RASi. There was no increase in need for dialysis or differences in congestive cardiac failure, cerebrovascular events, venous thromboembolism, hospitalization, intensive care admission, inotropes, or mechanical ventilation. Conclusions This meta‐analysis of randomized controlled trials evaluating angiotensin‐converting enzyme inhibitors/angiotensin II receptor blockers versus control in patients with COVID‐19 found no difference in all‐cause mortality, a borderline decrease in myocardial infarction, and an increased risk of acute kidney injury with RASi. Our findings provide strong evidence that RASi can be used safely in patients with COVID‐19.

Background: The concept of metabolic syndrome (MetSy) brings together components that individually represent a risk factor for cardiovascular diseases, which over time can prove to be more harmful if a combined effect of these is exhibited. Method: A single-centre retrospective study in an academic medical unit was conducted. We analysed the link between the MetSy and the occurrence of neuropsychic complications among atrial fibrillation (AF) patients. We sifted through the files of the patients admitted during 2015–2016 to the Municipal Emergency University Hospital Timisoara, Romania, with the diagnosis of AF. We divided these AF patients into two groups: the first group comprised patients with atrial fibrillation and MetSy (267 patients), while the second group comprised AF patients without MetSy (843 patients). We analysed the occurrence of neuropsychic changes (stroke, Parkinson’s disease, dementia, cognitive impairment, and silent lacunar infarction) among the two groups. Results: Cognitive impairment (p-value = 0.0081) and dementia (p-value < 0.0001) were less frequent in patients with AF and MetSy than in those with AF without MetSy. Regarding the presence of stroke and Parkinson’s disease (PD), we could not demonstrate the existence of any statistically significant difference between the two groups. Using logistic regression (enter test), we found that MetSy might have a protective effect (OR = 0.4040, 95% CI [0.2132; 0.7654], p-value = 0.0054) for the occurrence of dementia in those patients. Furthermore, obesity was the only factor with a possible protective effect from all the constituents of the MetSy when analysed together (with a significance level of p-value = 0.0004 for the logistic regression). The protective effect of MetSy against stroke occurrence was supplementarily proven by a longer period of survival without stroke from the AF diagnosis (3.521 years, p = 0.0304) compared to patients with AF without MetSy (3.286 years to first stroke occurrence). Conclusions: Metabolic syndrome might offer protection against the occurrence of dementia among patients with AF, but no effect was noted when compared with the presence of stroke. Further studies on larger cohorts can help us reach a conclusion regarding the positive effects of the metabolic syndrome.

Alzheimer’s disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ(1-40), Aβ(1-42), t-tau, cleaved-tau (c-tau), p-tau181, p-tau231, p-tau381, and p-tau441). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).

Abstract Aims In the EMPA‐REG OUTCOME trial, in patients with type 2 diabetes and established atherosclerotic cardiovascular (CV) disease, empagliflozin vs. placebo reduced the risk of hospitalization for heart failure (HHF) by 35%, CV death/HHF by 34%, and CV death by 38%, with an early separation of the cumulative incidence curves. We explored at what time point after randomization these benefits became apparent. Methods and results We expressed time trajectories for the effect of pooled empagliflozin doses vs. placebo on HHF, CV death/HHF, and CV death based on hazard ratios (95% confidence interval) and calculated the hazard ratio on the day the effect reached significance using Cox proportional hazards models. Overall, 7020 patients aged ≥18 years were treated with empagliflozin 10 mg (N = 2345), empagliflozin 25 mg (N = 2342), or placebo (N = 2333) once daily in addition to standard of care. Mean age (years ± SD) was 63.1 ± 8.6, and 72% were male. The benefit of empagliflozin on CV death first reached statistical significance on Day 59 (HR [95% confidence interval]) (0.28 [0.08, 0.96], P = 0.0424) and was generally sustained throughout the trial (overall 0.62 [0.49, 0.77], P

Abstract Aims Associations between growth differentiation factor‐15 (GDF‐15), cardiovascular outcomes, and exercise capacity among patients with a recent hospitalization for heart failure (HHF) and heart failure with reduced ejection fraction (HFrEF) are unknown. We utilized data from the ‘Functional Impact of GLP‐1 for Heart Failure Treatment’ (FIGHT) study to address these knowledge gaps. Methods and results FIGHT was a randomized clinical trial testing the effect of liraglutide (vs. placebo) among 300 participants with HFrEF and a recent HHF. Multivariable regression models evaluated associations between baseline GDF‐15 and change in GDF‐15 (per 1000 pg/mL increase from baseline to 30 days) with clinical outcomes (at 180 days) and declines in exercise capacity (6 min walk distance ≥ 45 m). At baseline (n = 249), median GDF‐15 value was 3221 pg/mL (interquartile range 1938–5511 pg/mL). Participants in the highest tertile of baseline GDF‐15 were more likely to be male and have more co‐morbidities. After adjustment, an increase in GDF‐15 over 30 days was associated with higher risk of death or HHF [hazard ratio 1.35, 95% confidence interval (CI) 1.11–1.64]. In addition, higher baseline GDF‐15 (per 1000 pg/mL until 6000 pg/mL) and an increase in GDF‐15 over 30 days were associated with declining 6 min walk distance (odds ratio 1.26, 95% CI 1.02–1.55 and odds ratio 1.37, 95% CI 1.12–1.69, respectively). GDF‐15 levels remained stable among participants randomized to liraglutide. Conclusions An increase in GDF‐15 over 30 days among patients in HFrEF was independently associated with an increased risk of cardiovascular events and declining exercise capacity. These results support the value of longitudinal GDF‐15 trajectory in informing risk of heart failure disease progression.

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

Aspects towards the area of array signal processing are majorly confined to two techniques, Direction of arrival (DOA) estimation and adaptive beamforming (ABF). There exist different traditional techniques for estimating the direction of incoming signals such as spectral and Eigen structure-based methods that find the direction of incoming signals. The major drawback of these techniques are that they fail to find the direction of the incoming signal in environments of low signal to noise (SNR). The maximum likelihood (ML) method has an upper hand in terms of statistical performance as compared to conventional methods and finds the direction of signal in low SNR conditions. In this article, the chicken swarm optimization (CSO) algorithm is explored for the optimization of ML function to find the direction of signals in uniform linear arrays (ULA). The algorithm is inspected with respect to the root mean square error (RMSE) and the probability of resolution (PR). Simulation results of the proposed technique prove that the ML-CSO algorithm outperforms other heuristic approaches such as the flower pollination algorithm (FPA) and other conventional techniques such as Capon, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm in lower SNR environment.

Current data indicate the existence of post-acute COVID-19 syndrome frequently expressing as cardiovascular and respiratory health issues. The long-term evolution of these complications is not yet fully known or predictable. Among the most common clinical manifestations of post-acute COVID-19 syndrome are dyspnea, palpitations, and fatigue, in most cases being transient and without underlying any morphological or functional changes. A single-center retrospective observational study was performed on cases that had presented with new-onset cardiac symptoms post-COVID-19 infection. Records of three male patients without pre-existing chronic cardiovascular pathology who had presented for dyspnea, fatigue, and palpitations around four weeks post-COVID-19 acute phase were studied in detail. The three post-COVID-19 cases exhibited arrhythmic complications after completely healing from the acute phase of the infection. Palpitations, along with chest pain, and possible aggravation or appearance of dyspnea, with syncopal episodes, were found to be present. All the three cases were non-vaccinated against COVID-19 infection. Isolated case reports showing arrhythmic complications such as atrial fibrillation and ventricular tachycardia on a small number of patients with these complications indicate the need for arrhythmic evaluation of large groups of patients in the post-acute stage of the COVID-19 syndrome for a better understanding of the phenomenon and implicitly better care of these patients. It would also be useful to evaluate large groups of patients divided into vaccinated/non-vaccinated against COVID-19 categories to determine whether vaccination per se can provide protection in the occurrence of these types of complications.

Abstract Aims Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition, and tissue congestion manifested by oedema is not present in all patients. We compared clinical characteristics, exercise capacity, and outcomes in patients with HFpEF with and without oedema. Methods and results This study was a post hoc analysis of pooled data of patients with left ventricular ejection fraction of ≥50% enrolled in the DOSE, CARRESS‐HF, RELAX, ATHENA, ROSE, INDIE, and NEAT trials. Patients were dichotomized by the severity of oedema. Cox proportional hazard regression and generalized linear regression models were used to assess associations between oedema, symptoms, and clinical outcomes. The ambulatory cohort included 393 patients (228 with and 165 without oedema), and the hospitalized cohort included 338 patients (249 with ≥moderate oedema and 89 with mild or none). Among ambulatory patients, patients with oedema had a higher body mass index (35.2 kg/m2 [inter‐quartile range, IQR 30.5, 41.6] vs. 31.6 kg/m2 [IQR 27.9, 36.3], P

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

This paper investigates the efficacy of an optimized fuzzy logic controller for real-time swing-up control and stabilization to a rigidly coupled twin-arm inverted pendulum system. The proposed fuzzy controller utilizes Lyapunov criteria for controller design to ensure system stability. The membership functions are further optimized based on the entropy function. The controller design is based on the black-box approach, eliminating the need for an accurate mathematical model of the system. The experimental results shows an improvement in the transient and steady-state response of the controlled system as compared to other state-of-the-art controllers. The proposed controller exhibits a small settling time of 4.0 s and reaches the stable swing-up position within 5 oscillations. Various error indices are evaluated that validates an overall improvement in the performance of the system.

Smoothed functional (SF) algorithm estimates the gradient of the stochastic optimization problem by convolution with a smoothening kernel. This process helps the algorithm to converge to a global minimum or a point close to it. We study a two-time scale SF based gradient search algorithm with Nesterov’s acceleration for stochastic optimization problems. The main contribution of our work is to prove the convergence of this algorithm using the stochastic approximation theory. We propose a novel Lyapunov function to show the associated second-order ordinary differential equations’ (o.d.e.) stability for a non-autonomous system. We compare our algorithm with other smoothed functional algorithms such as Quasi-Newton SF, Gradient SF and Jacobi Variant of Newton SF on two different optimization problems: first, on a simple stochastic function minimization problem, and second, on the problem of optimal routing in a queueing network. Additionally, we compared the algorithms on real weather data in a weather prediction task. Experimental results show that our algorithm performs significantly better than these baseline algorithms.

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum ( $6.83\times 10 ^{-4}$ , $2.06\times 10 ^{-4}$ , and $4.48\times 10 ^{-6}$ ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

Background Clinical prediction models have been developed for hospitalization for heart failure in type 2 diabetes. However, a systematic evaluation of these models’ performance, applicability, and clinical impact is absent. Methods and Results We searched Embase, MEDLINE, Web of Science, Google Scholar, and Tufts’ clinical prediction registry through February 2021. Studies needed to report the development, validation, clinical impact, or update of a prediction model for hospitalization for heart failure in type 2 diabetes with measures of model performance and sufficient information for clinical use. Model assessment was done with the Prediction Model Risk of Bias Assessment Tool, and meta‐analyses of model discrimination were performed. We included 15 model development and 3 external validation studies with data from 999 167 people with type 2 diabetes. Of the 15 models, 6 had undergone external validation and only 1 had low concern for risk of bias and applicability (Risk Equations for Complications of Type 2 Diabetes). Seven models were presented in a clinically useful manner (eg, risk score, online calculator) and 2 models were classified as the most suitable for clinical use based on study design, external validity, and point‐of‐care usability. These were Risk Equations for Complications of Type 2 Diabetes (meta‐analyzed c‐statistic, 0.76) and the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (meta‐analyzed c‐statistic, 0.78), which was the simplest model with only 5 variables. No studies reported clinical impact. Conclusions Most prediction models for hospitalization for heart failure in patients with type 2 diabetes have potential concerns with risk of bias or applicability, and uncertain external validity and clinical impact. Future research is needed to address these knowledge gaps.

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.

Abstract Background The EXAMINE trial tested the efficacy and safety of alogliptin, an inhibitor of dipeptidyl peptidase 4, compared with placebo in 5380 patients with type 2 diabetes and a recent acute coronary syndrome. Because alogliptin is cleared by the kidney, patients were stratified according to screening renal function within two independently randomized strata: (1) estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73m2 and (2) eGFR

Abstract Aims Sodium–glucose co‐transporter (SGLT)‐2 inhibitors have been shown to reduce the risk of cardiovascular death and heart failure (HF) hospitalization in patients with type 2 diabetes mellitus (DM) and high cardiovascular risk in two large clinical outcome trials: empagliflozin in EMPA‐REG OUTCOME and canagliflozin in CANVAS. The scope of eligibility for SGLT‐2 inhibitors (empagliflozin and canagliflozin) among patients with type 2 DM and HF, based on clinical trial criteria and current US Food and Drug Administration (FDA) labelling criteria, remains unknown. Methods and results Using data from the US Get With The Guidelines (GWTG)—Heart Failure registry, we evaluated the proportion of patients with DM and HF eligible for SGLT‐2 inhibitor therapy based on the clinical trial criteria and the US FDA labelling criteria. The GWTG‐HF registry is a quality improvement registry of patients admitted in hospital with HF in the USA. We included GWTG‐HF registry participants meeting eligibility criteria hospitalized between August 2014 and 30 June 2017 from sites fully participating in the registry. The initial inclusion time point reflects when both drugs had FDA approval. Among the 139 317 patients (out of 407 317) with DM hospitalized with HF (in 460 hospitals; 2014 to 2017), the median age was 71 years, 47% (n = 65 685) were female, and 43% (n = 59 973) had HF with reduced ejection fraction. Overall, 43% (n = 59 943) were eligible for the EMPA‐REG OUTCOME trial, 45% (n = 62 818) were eligible for the CANVAS trial, and 34% (n = 47 747) of patients were eligible for either SGLT‐2 inhibitors based on the FDA labelling criteria. Among the FDA‐eligible patients, 91.5% (n = 43 708) were eligible for either the EMPA‐REG OUTCOME trial or the CANVAS trial. Patients who were FDA eligible, compared with those who were not, were younger (70.0 vs. 72.0 years of age), more likely to be male (57.7 vs. 50.3%), and had less burden of co‐morbidities. Conclusions The majority of patients with DM who are hospitalized with HF are not eligible for SGLT‐2 inhibitor therapies. Ongoing studies evaluating the safety and efficacy of SGLT‐2 inhibitors among patients with HF may potentially broaden the population that may benefit from these therapies.

Background Unlike patients with low ejection fraction after an acute coronary syndrome (ACS), little is known about the long‐term incidence and influence of cardiovascular events before sudden death among stabilized patients after ACS. Methods and Results A total of 18 144 patients stabilized within 10 days after ACS in IMPROVE‐IT (Improved Reduction of Outcomes: Vytorin Efficacy International Trial) were studied. Cumulative incidence rates (IRs) and IRs per 100 patient‐years of sudden death were calculated. Using Cox proportional hazards, the association of ≥1 additional postrandomization cardiovascular events (myocardial infarction, stroke, and hospitalization for unstable angina or heart failure) with sudden death was examined. Early (≤1 year after ACS) and late sudden deaths (>1 year) were compared. Of 2446 total deaths, 402 (16%) were sudden. The median time to sudden death was 2.7 years, with 109 early and 293 late sudden deaths. The cumulative IR was 2.47% (95% CI, 2.23%–2.73%) at 7 years of follow‐up. The risk of sudden death following a postrandomization cardiovascular event (150/402 [37%] sudden deaths; median 1.4 years) was greater (IR/100 patient‐years, 1.45 [95% CI, 1.23–1.69]) than the risk with no postrandomization cardiovascular event (IR/100 patient‐years, 0.27 [95% CI, 0.24–0.30]). Postrandomization myocardial infarction (hazard ratio [HR], 3.64 [95% CI, 2.85–4.66]) and heart failure (HR, 4.55 [95% CI, 3.33–6.22]) significantly increased future risk of sudden death. Conclusions Patients stabilized within 10 days of an ACS remain at long‐term risk of sudden death with the greatest risk in those with an additional cardiovascular event. These results refine the long‐term risk and risk effectors of sudden death, which may help clinicians identify opportunities to improve care. Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00202878.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is one of the top 10 causes of death worldwide. Drug-resistant tuberculosis (DR-TB) poses a major threat to the World Health Organization's “End TB” strategy which has defined its target as the year 2035. In 2019, there were close to 0.5 million cases of DRTB, of which 78% were resistant to multiple TB drugs. The traditional culture-based drug susceptibility test (DST - the current gold standard) often takes multiple weeks and the necessary laboratory facilities are not readily available in low-income countries. Whole genome sequencing (WGS) technology is rapidly becoming an important tool in clinical and research applications including transmission detection or prediction of DR-TB. For the latter, many tools have recently been developed using curated database(s) of known resistance conferring mutations. However, documenting all the mutations and their effect is a time-taking and a continuous process and therefore Machine Learning (ML) techniques can be useful for predicting the presence of DR-TB based on WGS data. This can pave the way to an earlier detection of drug resistance and consequently more efficient treatment when compared to the traditional DST.

Lymphangioma is a benign tumor characterized by proliferation of thin-walled lymphatic spaces. Lymphangioma of the small-bowel mesentery is rare, with an incidence of 1 : 250,000, representing less than 1% of all lymphangiomas. The predilection of the tumor is in the head and neck (70%), axillary (20%), and internal organs (10%). They are usually asymptomatic but can cause acute abdominal symptoms due to complications such as volvulus, bleeding, or lymphangioma rupture that require emergent surgery. Here, we report a case of mesenteric lymphangioma (ML) of a small bowel in a paediatric patient who presented with pain abdomen on and off which increased in severity and later had features of subacute intestinal obstruction. He underwent explorative laparotomy, and the mass was excised completely along with the part of small intestine. Pathological analysis of the surgical specimen confirmed the diagnosis of ML of the small intestine. The postoperative recovery was uneventful, and the patient was discharged after ten days of hospital stay. Though benign in nature, ML may cause acute abdominal symptoms that require emergent surgery. Therefore, it has to be kept in differential diagnosis of the acute abdominal condition.