Category: Nevin Manimala

BMC Anesthesiol. 2023 Jul 21;23(1):245. doi: 10.1186/s12871-023-02135-8.

ABSTRACT

BACKGROUND: The number of non-intubated general anesthesia outside the operating room is growing as the increasing demand for comfort treatment. Non-intubated general anesthesia outside the operating room requires rapid onset of anesthesia, smoothness, quick recovery, and few postoperative complications. Traditional anesthetic regimens (propofol alone or propofol and opioids/dezocine/midazolam, etc.) have severe respiratory and circulatory depression and many systemic adverse effects. In this paper, we compare the effectiveness and safety of propofol and subclinical doses of esketamine with other traditional regimens applied to non-intubated general anesthesia through a systematic review and meta-analysis.

METHODS: We searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and Sinomed databases for the period from January 2000 to October 2022. We rigorously screened the literature according to predefined inclusion and exclusion criteria, while risk assessment of the studies was performed using The Cochrane Collaboration’s tool, and statistical analysis of the data was performed using RevMan 5.4 software. The main outcome indicators we evaluated were the various hemodynamic parameters and incidence of various adverse effects between the experimental and control groups after induction of anesthesia.

RESULTS: After a rigorous screening process, a total of 14 papers were included in the final meta-analysis. After risk bias assessment, three of the papers were judged as low risk and the others were judged as having moderate to high risk. Forest plots were drawn for a total of 16 indicators. Meta-analysis showed statistically significant differences in HR’ WMD 3.27 (0.66, 5.87), MAP’ WMD 9.68 (6.13, 13.24), SBP’ WMD 5.42 (2.11, 8.73), DBP’ WMD 4.02 (1.15, 6.88), propofol dose’ SMD -1.39 (-2.45, -0.33), hypotension’ RR 0.30 (0.20, 0.45), bradycardia’ RR 0.33 (0.14, 0.77), hypoxemia or apnea’ RR 0.45 (0.23, 0.89), injection pain’ RR 0.28 (0.13, 0.60), intraoperative choking’ RR 0.62 (0.50, 0.77), intraoperative body movements’ RR 0.48 (0.29, 0.81) and overall incidence of adverse reactions’ RR 0.52 (0.39, 0.70).The indicators that were not statistically different were time to wake up’ WMD – 0.55 (-1.29, 0.19), nausea and vomiting 0.84′ RR (0.43, 1.67), headache and dizziness’ RR 1.57 (0.98, 2.50) and neuropsychiatric reaction’ RR 1.05 (0.28, 3.93). The funnel plot showed that the vast majority of studies fell within the funnel interval, but the symmetry was relatively poor.

CONCLUSION: In non-intubated general anesthesia, the combination of subclinical doses of esketamine and propofol did reduce circulatory and respiratory depression, injection pain, and other adverse effects, while the incidence of esketamine’s own side effects such as neuropsychiatric reactions did not increase, and the combination of the two did not cause the occurrence of new and more serious adverse reactions, and the combination of the two was safe and effective.

TRIAL REGISTRATION: PROSPREO registration number: CRD 42022368966.

PMID:37479982 | DOI:10.1186/s12871-023-02135-8

Phys Rev Lett. 2023 Jul 7;131(1):011801. doi: 10.1103/PhysRevLett.131.011801.

ABSTRACT

Neutrino-induced charged-current single π^{+} production in the Δ(1232) resonance region is of considerable interest to accelerator-based neutrino oscillation experiments. In this Letter, high statistic differential cross sections are reported for the semiexclusive reaction ν_{μ}A→μ^{-}π^{+}+ nucleon(s) on scintillator, carbon, water, iron, and lead targets recorded by MINERvA using a wideband ν_{μ} beam with ⟨E_{ν}⟩≈6 GeV. Suppression of the cross section at low Q^{2} and enhancement of low T_{π} are observed in both light and heavy nuclear targets compared with phenomenological models used in current neutrino interaction generators. The cross sections per nucleon for iron and lead compared with CH across the kinematic variables probed are 0.8 and 0.5 respectively, a scaling which is also not predicted by current generators.

PMID:37478458 | DOI:10.1103/PhysRevLett.131.011801

Phys Rev Lett. 2023 Jul 7;131(1):016601. doi: 10.1103/PhysRevLett.131.016601.

ABSTRACT

Topological entanglement entropy (TEE) is a key diagnostic of topological order, allowing one to detect the presence of Abelian or non-Abelian anyons. However, there are currently no experimentally feasible protocols to measure TEE in condensed matter systems. Here, we propose a scheme to measure the TEE of chiral topological phases, carrying protected edge states, based on a nontrivial connection with the thermodynamic entropy change occurring in a quantum point contact (QPC) as it pinches off the topological liquid into two. We show how this entropy change can be extracted using Maxwell relations from charge detection of a nearby quantum dot. We demonstrate this explicitly for the Abelian Laughlin states, using an exact solution of the sine-Gordon model describing the universal crossover in the QPC. Our approach might open a new thermodynamic detection scheme of topological states also with non-Abelian statistics.

PMID:37478453 | DOI:10.1103/PhysRevLett.131.016601

Phys Rev Lett. 2023 Jul 7;131(1):012301. doi: 10.1103/PhysRevLett.131.012301.

ABSTRACT

The hadrochemistry of bottom quarks (b) produced in hadronic collisions encodes valuable information on the mechanism of color neutralization in these reactions. Since the b-quark mass is much larger than the typical hadronic scale of ∼1 GeV, bb[over ¯] pair production is expected to be well separated from subsequent hadronization processes. A significantly larger fraction of b baryons has been observed in proton-proton (pp) and proton-antiproton (pp[over ¯]) reactions relative to e^{+}e^{-} collisions, challenging theoretical descriptions. We address this problem by employing a statistical hadronization approach with an augmented set of b-hadron states beyond currently measured ones, guided by the relativistic quark model and lattice-QCD computations. Assuming relative chemical equilibrium between different b-hadron yields, thermal densities are used as fragmentation weights of b quarks into various hadron species. With quark model estimates of the decay patterns of excited states, the fragmentation fractions of weakly decaying b hadrons are computed and found to agree with measurements in pp[over ¯] collisions at the Tevatron. By combining transverse-momentum (p_{T}) distributions of b quarks from perturbative QCD with thermal weights and independent fragmentation toward high p_{T}, a fair description of the p_{T}-dependent B[over ¯]_{s}^{0}/B^{-} and Λ_{b}^{0}/B^{-} ratios measured in pp collisions at the LHC is obtained. The observed enhancement of Λ_{b}^{0} production is attributed to the feeddown from thus far unobserved excited b baryons. Finally, we implement the hadrochemistry into a strongly coupled transport approach for b quarks in heavy-ion collisions, utilizing previously determined b-quark transport coefficients in the quark-gluon plasma, to highlight the modifications of hadrochemistry and collective behavior of b hadrons in Pb-Pb collisions at the LHC.

PMID:37478427 | DOI:10.1103/PhysRevLett.131.012301

Lab Med. 2023 Jul 21:lmad059. doi: 10.1093/labmed/lmad059. Online ahead of print.

ABSTRACT

BACKGROUND: Transfusion services aim to maintain sufficient blood inventory to support patients, even with challenges introduced by COVID-19.

OBJECTIVES: To review blood usage and wastage before, during, and after COVID-19 surges, and to evaluate effects on inventory.

METHODS: In a retrospective review, we evaluated the association between time periods corresponding to the initial wave of COVID-19 (pre-COVID-19, quarantine, and postquarantine) and blood usage/wastage. Data were stratified by period, and χ2 testing was used to examine the association between these time periods and blood usage/wastage.

RESULTS: In the period before COVID-19, the transfusion service used more units, and in the period after quarantine, more units went to waste. Across all time periods, the most-used product was RBCs, and the most wasted product was plasma. A statistically significant association existed between usage (χ2 [6/3209 (0.2%)]) = 24.534; P ≤.001; Cramer V = 0.62), wastage (χ2 [6/775 (0.8%)]) = 21.673; P = .001; Cramer V = 0.118), and time period. The postquarantine period displayed the highest wastage costs ($51,032.35), compared with the pre-COVID-19 period ($29,734.45).

CONCLUSION: Changes in blood inventory use and waste are significantly associated with the onset and continuation of COVID-19.

PMID:37478411 | DOI:10.1093/labmed/lmad059

Inflamm Bowel Dis. 2023 Jul 21:izad131. doi: 10.1093/ibd/izad131. Online ahead of print.

ABSTRACT

BACKGROUND: Active smoking is a well-established risk factor for developing Crohn’s disease (CD) and negatively impacts overall disease progression. Patients who start or continue smoking after CD diagnosis are at risk for poor outcomes, higher therapeutic requirements, and have higher rates of relapse. However, it remains unclear if the exposure to smoking leads to increased sequencing through treatment therapies, especially biologics.

METHODS: The Study of Prospective Adult Research Cohort with IBD (SPARC IBD) registry has been collecting patient-reported outcomes data in real-time, as well as laboratory, endoscopic, and pathologic samples from 17 tertiary referral centers since 2016. In this study, we conducted a retrospective review of the SPARC clinical registry collected between December 2016 and January 2021 from 1 participating site, the University of Maryland School of Medicine’s Inflammatory Bowel Disease Program. A total of 619 patients were enrolled in the SPARC IBD database. Four hundred twenty-five patients with CD were included for initial review of completeness of data; of these, 144 patients were excluded due to missing data on smoking status and/or biologic treatment, resulting in a final cohort of 281 patients. We collected and analyzed baseline demographic and clinical characteristics. The final cohort was categorized into 3 exposure groups: current, former, and never smokers. Our outcome of interest was number biologics used, categorized into 3 groups: 0, 1, or ≥2 biologics.

RESULTS: One hundred seventy-two never smokers, 70 former smokers, and 39 current smokers were identified. Current, former, and never smokers had no statistically significant differences in number of biologics used (ie, biologic sequencing). However, statistically significant independent risk factors for increased sequencing of biologics were identified. These risk factors included female sex, ileocolonic disease location, younger age at diagnosis, and prolonged disease duration; none of these factors remained significant in adjusted analyses.

CONCLUSION: To date, this is the first study assessing the association of smoking and sequencing of biologics. Although current or former smokers were not found to sequence through more biologics when compared with never smokers, smoking is a well-established risk factor for poor health outcomes, and efforts should be made to counsel patients to quit. Further, additional research must be done to stratify risk to patients based on amount of tobacco exposure.

PMID:37478408 | DOI:10.1093/ibd/izad131

Cereb Cortex. 2023 Jul 21:bhad291. doi: 10.1093/cercor/bhad291. Online ahead of print.

NO ABSTRACT

PMID:37478404 | DOI:10.1093/cercor/bhad291

Brief Bioinform. 2023 Jul 21:bbad253. doi: 10.1093/bib/bbad253. Online ahead of print.

ABSTRACT

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

PMID:37478378 | DOI:10.1093/bib/bbad253

Brief Bioinform. 2023 Jul 21:bbad236. doi: 10.1093/bib/bbad236. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can be a challenge for both end-users and decision-makers, as well as AI developers. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties but also legally required for AI systems that can have a significant impact on human lives. Fairness is another growing concern, as algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes. Explainable AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

PMID:37478371 | DOI:10.1093/bib/bbad236

J Phys Chem B. 2023 Jul 21. doi: 10.1021/acs.jpcb.3c02505. Online ahead of print.

ABSTRACT

A mean field theory model describing the interaction of ion hydration layers with the network of hydrogen bonds of both water and the nonionic polymer poly(ethylene oxide) (PEO) is presented. The predictions of the model for types and statistics of hydrogen bonds, the number of water molecules bound to PEO, or their dependence on temperature are successfully verified from all-atom simulations at different NaCl and PEO concentrations. Furthermore, our simulations show that the binding of cations to PEO increases monotonically with salt concentration, in agreement with recent experimental results, through a mechanism in which the sum of the number of bound water and cations is independent of salt concentration. The model introduced is general and can describe any salt or hydrogen-bond-forming polymer.

PMID:37478338 | DOI:10.1021/acs.jpcb.3c02505