Outpatient insulin use in type 2 diabetes mellitus and acute respiratory distress syndrome outcomes:A retrospective cohort study

BACKGROUND The impact of type 2 diabetes mellitus(T2DM)on acute respiratory distress syndrome(ARDS)is debatable.T2DM was suspected to reduce the risk and complications of ARDS.However,during coronavirus disease 2019(COVID-19),T2DM predisposed patients to ARDS,especially those who were on insulin at home.AIMTo evaluate the impact of outpatient insulin use in T2DM patients on non-COVID-19 ARDS outcomes.METHODS We conducted a retrospective cohort analysis using the Nationwide Inpatient Sample database.Adult patients diagnosed with ARDS were stratified into insulin-dependent diabetes mellitus(DM)(IDDM)and non-insulindependent DM(NIDDM)groups.After applying exclusion criteria and matching over 20 variables,we compared cohorts for mortality,duration of mechanical ventilation,incidence of acute kidney injury(AKI),length of stay(LOS),hospitalization costs,and other clinical outcomes.RESULTS Following 1:1 propensity score matching,the analysis included 274 patients in each group.Notably,no statistically significant differences emerged between the IDDM and NIDDM groups in terms of mortality rates(32.8%vs 31.0%,P=0.520),median hospital LOS(10 d,P=0.537),requirement for mechanical ventilation,incidence rates of sepsis,pneumonia or AKI,median total hospitalization costs,or patient disposition upon discharge.CONCLUSION Compared to alternative anti-diabetic medications,outpatient insulin treatment does not appear to exert an independent influence on in-hospital morbidity or mortality in diabetic patients with non-COVID-19 ARDS.

Systemic oncological therapy in breast cancer patients on dialysis

The advancement of renal replacement therapy has significantly enhanced the survival rates of patients with end-stage renal disease(ESRD)over time.How-ever,this prolonged survival has also been associated with a higher likelihood of cancer diagnoses among these patients including breast cancer.Breast cancer treatment typically involves surgery,radiation,and systemic therapies,with ap-proaches tailored to cancer type,stage,and patient preferences.However,renal replacement therapy complicates systemic therapy due to altered drug clearance and the necessity for dialysis sessions.This review emphasizes the need for opti-mized dosing and administration strategies for systemic breast cancer treatments in dialysis patients,aiming to ensure both efficacy and safety.Additionally,ch-allenges in breast cancer screening and diagnosis in this population,including soft-tissue calcifications,are highlighted.

Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare application.In this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modified version of mel fre-quency cepstral coefficients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ficient,hurst exponent,and root mean square.Several state-of-the-art classifiers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features.Comparison-based analyses of these classifiers have also been considered based on total classi-fication accuracy,prediction speed(observations/sec),and processing time.The proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG channels.SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

Synthesis of Ni^2+cation modified TS-1 molecular sieve nanosheets as effective photocatalysts for alcohol oxidation and pollutant degradation

lmprovement of the charge separation of titanosilicate molecular sieves is critical to their use asphotocatalysts for oxidative organic transformations.In this work,MFI TS-1 molecular sievenanosheets(TS-1 NS)were synthesized by a low-temperature hydrothermal method using a tai-lored diquaternary ammonium surfactant as the structure-directing agent.Introducing Ni^2+cationsat the ion-exchange sites of the TS-1 NS framework significantly enhanced its photoactivity in aero-bic alcohol oxidation.The optimized Ni cation-functionalized TS-1 NS(Ni/TS-1 NS)provide impres-sive photoactivity,with a benzyl alcohol(BA)conversion of 78.9%and benzyl aldehyde(BAD)se-lectivity of 98.8%using O as the only oxidant under full light irradiation;this BAD yield is approx-imately six times greater than that obtained for bulk TS-1,and is maintained for five runs.The ex-cellent photoactivity of Ni/TS-1 NS is attributed to the significantly enlarged surface area of thetwo-dimensional morphology TS-1 NS,extra mesopores,and greatly improved charge separation.Compared with bulk TS-1,Ni/TS-1 NS has a much shorter charge transfer distance.Theas-introduced Ni species could capture the photoelectrons to further improve the charge separa-tion.This work opens the way to a class of highly selective,robust,and low-cost titanosilicate mo-lecular sieve-based photocatalysts with industrial potential for selective oxidative transformationsand pollutant degradation.

Comparative Review of Drugs Used in Diabetes Mellitus—New and Old

Background: Diabetes mellitus (DM) is a syndrome of chronically elevated glucose level in the blood either due to insulin resistance, insulin deficiency or both. In addition, it may occur due to defective metabolism of carbohydrates, fats and proteins. There are 3 main types of DM: Type 2 DM is more prevalent in adults and is typically due to relative insulin deficiency, deficiency of insulin in children leads to DM type 1;and lastly, gestational diabetes occurs during pregnancy resulting from an imbalance of placental hormones. Introduction: Insulin, Biguanides and Sulfonylureas are some of the drug classes used to treat DM. However, their use is complicated by numerous side effects, such as;hypoglycemia & weight gain from insulin and sulfonylureas;lactic acidosis, vitamin B12 deficiency and gastrointestinal upset with metformin. Route of administration and cost are also important factors to consider when prescribing. It is for this reason the quest for newer, safer and easier to administer drugs is ongoing. Methodology: Used all the articles available on anti Diabetic drugs on web especially in British Medical Journal, Elsevier, Pubmed, Google scholar and Wikipedia etc. Got a final review article to compare the older and newer anti Diabetic drugs. Results and Conclusion: Insulin is good for controlling acute hyperglycemic states in DM but it causes acute hypoglycemia and lipodystrophy. Metformin is good hypoglycemic and easily available but causes hypoglycemia, metallic taste, Lactic acidosis and B12 deficiency. Sulfonylureas are good hypoglycemic but causes severe hypoglycemia acutely and weight gain so contraindicated for obese or hypertensive patients. While newer antidiabetics such as GLP 1 agonists increases insulin secretions has very low risk of hypoglycemia, causes weight loss as compared to insulin and decreases risk of cardiovascular side effects but still can’t be used in renally impaired patients, causes pancreatitis and can not be given in gastroparesis patients, similarly a newer drug of this class known as LY2189265 has long halflife of 90 hours, better efficacy, but causes pancreatitis and increase diastolic BP in high doses, pancreatitis is not associated with lixisenatide (GLP 1 agonist), while DPP4 inhibitors which increases GLP 1 in body has less risk of hypoglycemia, GI side effects, are weight neutral can be used in CKD but causes headaches and Nasopharyngitis. Bromocriptine or pegvisomant are used in patients of growth hormones adenoma induced DM as a medical therapy but are associated with psychosis and hallucinations. Meglitinides increases insulin secretion and has minuscule risk of hypoglycemia but can not be used in CKD patients. Otelixizumab and Teplizumab decrease T cell functions and save beta cells from immune reactions used in DM 1 but cause immune suppression and is an orphan drug. Recombinant GAD used in vaccines decreased antibody mediated beta cell damage but is still under studies.

Effects of three coniferous plantation species on plant-soil feedbacks and soil physical and chemical properties in semiarid mountain ecosystems

Background:Large-scale afforestation can significantly change the ground cover and soil physicochemical properties,especially the soil fertility maintenance and water conservation functions of artificial forests,which are very important in semi-arid mountain ecosystems.However,how different tree species affect soil nutrients and soil physicochemical properties after afforestation,and which is the best plantation species for improving soil fertility and water conservation functions remain largely unknown.Methods:This study investigated the soil nutrient contents of three different plantations(Larix principis-rupprechtii,Picea crassifolia,Pinus tabuliformis),soils and plant-soil feedbacks,as well as the interactions between soil physicochemical properties.Results:The results revealed that the leaves and litter layers strongly influenced soil nutrient availability through biogeochemical processes:P.tabuliformis had higher organic carbon,ratio of organic carbon to total nitrogen(C:N)and organic carbon to total phosphorus(C:P)in the leaves and litter layers than L.principis-rupprechtii or P.crassifolia,suggesting that higher C:N and C:P hindered litter decomposition.As a result,the L.principis-rupprechtii and P.crassifolia plantation forests significantly improved soil nutrients and clay components,compared with the P.tabuliformis plantation forest.Furthermore,the L.principis-rupprechtii and P.crassifolia plantation forests significantly improved the soil capacity,soil total porosity,and capillary porosity,decreased soil bulk density,and enhanced water storage capacity,compared with the P.tabuliformis plantation forest.The results of this study showed that,the strong link between plants and soil was tightly coupled to C:N and C:P,and there was a close correlation between soil particle size distribution and soil physicochemical properties.Conclusions:Therefore,our results recommend planting the L.principis-rupprechtii and P.crassifolia as the preferred tree species to enhance the soil fertility and water conservation functions,especially in semi-arid regions mountain forest ecosystems.

用视频教学课程重塑教育

Khan学院因其大量的视频学习节目而为人所熟知。目前共计有2200个视频，它们涵盖了从基础算术到矢量微积分的全部内容。而且在你看到的一些视频中，每月都有上百万的学生在网站上使用。这是观看量在100到200000的视频排序。但在这里想谈的是，在这个领域如何能上升到更高的层面。在这个话题之前，先来回顾一下它是如何开始的。

Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy management.In smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart grids.In complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research challenge.These challenges can be resolved through various machine learning techniques and data analytics.In this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart grid.The proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution companies.In the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm optimization.The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

Optimal Resource Allocation in Fog Computing for Healthcare Applications

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication industry.Various IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and storage.However,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT devices.Consequently,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time applications.Therefore,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of milliseconds.This paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the IoHT.The proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load balancing.We consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average throughput.The proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been conducted.The experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

Serious games in science education:a systematic literature review

Teaching science through computer games,simulations,and artificial intelligence(AI)is an increasingly active research field.To this end,we conducted a systematic literature review on serious games for science education to reveal research trends and patterns.We discussed the role of virtual reality(VR),AI,and augmented reality(AR)games in teaching science subjects like physics.Specifically,we covered the research spanning between 2011 and 2021,investigated country-wise concentration and most common evaluation methods,and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.

Deep-piRNA:Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

Piwi-interacting Ribonucleic acids(piRNAs)molecule is a wellknown subclass of small non-codingRNAmolecules that are mainly responsible for maintaining genome integrity,regulating gene expression,and germline stem cell maintenance by suppressing transposon elements.The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development.Due to the vital roles of the piRNA in computational biology,the identification of piRNAs has become an important area of research in computational biology.This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods.The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process.The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method.The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59%and 2.81%at layer I and layer II respectively.It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine.

Quantum Monty Hall Problem under Decoherence

We study the effect of decoherence on quantum Monty Hall problem under the influence of amplitude damping, depolarizing, and dephasing channels. It is shown that under the effect of decoherence, there is a Nash equilibrium of the game in case of depolarizing channel for Alice＇s quantum strategy. Whereas in case of dephasing noise, the game is not influenced by the quantum channel. For amplitude damping channel, Bob＇s payoffs are found symmetrical about a decoherence of 50% and the maximum occurs at this value of decoherence for his classical strategy. However, it is worth-mentioning that in case of depolarizing channel, Bob＇s classical strategy remains always dominant against any choice of Alice＇s strategy.

Decoherence Effects on Multiplayer Cooperative Quantum Games

We study the behavior of cooperative multiplayer quantum games [Q. Chen, Y. Wang, J.T. Liu, and K.L. Wang, Phys. Lett. A 327 （2004） 98; A.P. Flitney and L.C.L. Hollenberg, Quantum Inf. Comput. 7 （2007） 111] in the presence of decoherence using different quantum channels such as amplitude damping, depolarizing and phase damping. It is seen that the outcomes of the games for the two damping channels with maximum values of decoherence reduce to same value. However, in comparison to phase damping channel, the payoffs of cooperators are strongly damped under the influence amplitude damping channel for the lower values of decoherence parameter. In the case of depolarizing channel, the game is a no-payoff game irrespective of the degree of entanglement in the initial state for the larger values of decoherence parameter. The deeoherenee gets the cooperators worse off.

Heart Disease Risk Prediction Expending of Classification Algorithms

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast volume of patient records,which are challenging to manage manually.When it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful information.Several methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment measures.However,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from UCI.The SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

Quantum Stackelberg Duopoly in a Noninertial Frame

We study the influence of the Unruh effect on quantum Stackelberg duopoly.It is shown that the acceleration of a noninertial frame strongly affects the payoffs of the firms.The validation of the subgame perfect Nash equilibrium is limited to a particular range of acceleration of the noninertial frame.The benefit of the initial state entanglement in the quantum form of the duopoly in the inertial frame is adversely affected by the acceleration.The duopoly can become a follower advantage only in a small region of the acceleration.

Non-covalent interaction of atomically dispersed dual-site catalysts featuring Co and Ni nascent pair sites for efficient electrocatalytic overall water splitting

The scarcity of highly effective and economical catalysts is a major impediment to the widespread adop-tion of electrochemical water splitting for the generation of hydrogen.MoS_(2),a low-cost candidate,suffers from inefficient catalytic activity.Nonetheless,a captivating strategy has emerged,which involves the en-gineering of heteroatom doping to enhance electrochemical proficiency.This investigation demonstrates a successful implementation of the strategy by combining ultrathin MoS_(2) nanosheets with Co and Ni dual single multi-atoms(DSMAs)grown directly on 2D N-doped carbon nanosheets(CoNi-MoS_(2)/NCNs)for the purpose of improving hydrogen evolution reaction(HER)and oxygen evolution reaction(OER).With the aid of a dual-atom doped bifunctional electrocatalyst,effective water splitting has been achieved across a broad pH range in electrolytes.The double doping of Co and Ni strengthens their interactions,thereby altering the electromagnetic composition of the host MoS_(2) and ultimately leading to improved electrocat-alytic activity.Additionally,the synergistic effect between NCNs and MoS_(2) nanosheets provided efficient electron transport channels for ions and an ample surface area with open voids for ion diffusion.Con-sequently,the CoNi-MoS_(2)/NCNs catalysts demonstrated exceptional stability and activity,producing low degree overpotentials of 180.5,124.9,and 196.4 mV for HER and 200,203,and 207 mV for OER in neu-tral,alkaline,and acidic mediums,respectively,while also exhibiting outstanding overall water-splitting performance,durability,and stability when used as an electrolyzer at universal pH.

Acatulides A-G,neuroprotective macrolides from Acaulium album H-JQSF

Fungal symbionts co-evolve with hosts and microbial co-inhabitants to acquire an unpredictable potential for producing novel bioactive metabolites,but the knowledge about the topic remains patchy and superficial.Here we present the chemical characterization of acatulides A-G(1-7)as architecturally unprecedented macrolides from the solid-state culture of Acaulium album H-JQSF,an arthropod-associated fungus.The acatulide structures were elucidated by spectroscopic analysis,modified Mosher's method and single-crystal X-ray diffraction.The plausible biosynthetic pathways for compounds 1-4 are proposed.Interestingly,acatulides B-D(2-4)and G(7)were demonstrated to be neuroprotective against the 1-methyl-4-phenylpyridinium(MPP+)-induced damage to SH-SY5Y cells and nematode Caenorhabditis elegans(C.elegans).

Seven drimane-type sesquiterpenoids from an earwig-associated Aspergillus sp.

Drimane-type sesquiterpenoids are widely distributed in fungi.From the ethyl acetate extract of the earwig-derived Aspergillus sp.NF2396,seven new drimane-type sesquiterpenoids,named drimanenoids A−G(1−7),were isolated.Their structures were elucidated by diverse spectroscopic analysis including high-resolution ESI-MS,one-and two-dimensional NMR spectroscopy.Drimanenoids A−F(1−6)are new members of drimane-type sesquiterpenoid esterified with unsaturated fatty acid side chain at C-6.Drimanenoids C(3),D(4)and F(6)showed antibacterial activity against five types of bacteria with different inhibition diameters.Drimanenoid D(4)exhibited moderate cytotoxicity against human myelogenous leukemia cell line K562 with an IC_(50) value of 12.88±0.11μmol·L^(−1).

How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.Notably,Bard has recently been updated to handle visual inputs alongside text prompts during conversations.Given Bard's impressive track record in handling textual inputs,we explore its capabilities in understanding and interpreting visual data(images)conditioned by text questions.This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models,especially in addressing complex computer vision problems that demand accurate visual and language understanding.Specifically,in this study,we focus on 15 diverse task scenarios encompassing regular,camouflaged,medical,under-water and remote sensing data to comprehensively evaluate Bard's performance.Our primary finding indicates that Bard still struggles in these vision scenarios,highlighting the significant gap in vision-based understanding that needs to be bridged in future developments.We expect that this empirical study will prove valuable in advancing future models,leading to enhanced capabilities in comprehending and interpreting finegrained visual data.Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand.

Non-Maximal Tripartite Entanglement Degradation of Dirac and Scalar Fields in Non-Inertial Frames

The π-tangle is used to study the behavior of entanglement of a nonmaximal tripartite state of both Dirac and scMar fields in accelerated frame. For Dirac fields, the degree of degradation with acceleration of both one-tangle of accelerated observer and π-tangle, for the same initial entanglement, is different by just interchanging the values of probability amplitudes. A fraction of both one-tangles and the π-tangle always survives for any choice of acceleration and the degree of initial entanglement. For scalar field, the one-tangle of accelerated observer depends on the choice of values of probability amplitudes and it vanishes in the range of infinite acceleration, whereas for 1r-tangle this is not always true. The dependence of π-tangle on probability amplitudes varies with acceleration. In the lower range of acceleration, its behavior changes by switching between the values of probability amplitudes and for larger values of acceleration this dependence on probability amplitudes vanishes. Interestingly, unlike bipartite entanglement, the degradation of π-tangle against acceleration in the case of sca/ar fields is slower than for Dirac fields.