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Abstract Suicide is a complex phenomenon that is often not preceded by a diagnosed mental health condition, therefore making it difficult to study and mitigate. Artificial Intelligence has increasingly been used to better understand Social Determinants of Health factors that influence suicide outcomes. In this review we find that many studies use limited SDoH information and minority groups are often underrepresented, thereby omitting important factors that could influence risk of suicide.

Secretory leukocyte protease inhibitor (SLPI) is an important cationic protein involved in innate airway immunity and highly expressed in mucosal secretions, shown to target and inhibit neutrophil elastase (NE), cathepsin G and trypsin activity to limit proteolytic activity. In addition to the potent anti-protease activity, SLPI has been demonstrated to exert a direct anti-inflammatory effect, which is mediated via increased inhibition and competitive binding of NF-κB, regulating immune responses through limiting transcription of pro-inflammatory gene targets. In muco-obstructive lung disorders, such as Chronic Obstructive Pulmonary Disease (COPD) and Cystic Fibrosis (CF), there is an observed elevation in airway SLPI protein concentrations as a result of increased lung inflammation and disease progression. However, studies have identified COPD patients presenting with diminished SLPI concentrations. Furthermore, there is a decrease in SLPI concentrations through cleavage and subsequent inactivation by NE degradation in Pseudomonas aeruginosa infected people with CF (pwCF). These observations suggest reduced SLPI protein levels may contribute to the compromising of airway immunity indicating a potential role of decreased SLPI levels in the pathogenesis of muco-obstructive lung disease. The Beta Epithelial Na+ Channel transgenic (ENaC-Tg) mouse model phenotype exhibits characteristics which replicate the pathological features observed in conditions such as COPD and CF, including mucus accumulation, alterations in airway morphology and increased pulmonary inflammation. To evaluate the effect of SLPI in muco-obstructive pulmonary disease, ENaC-Tg mice were crossed with SLPI knock-out (SLPI-/-) mice, generating a ENaC-Tg/SLPI-/- colony to further investigate the role of SLPI in chronic lung disease and determine the effect of its ablation on disease pathogenesis.

Background:. As HIV-positive individuals utilizing highly active antiretroviral therapy live longer, the burden of breast cancer increases in the population. Breast reconstruction is an integral aspect of surgical treatment for many patients after a breast cancer diagnosis, prompting this examination of the characteristics and outcomes of breast reconstruction in this growing patient population. Methods:. Using Merative MarketScan Research Databases, a large multipayer database, HIV-positive adult patients who underwent autologous or implant-based breast reconstruction between 2007 and 2021 were identified using International Classification of Disease codes and Common Procedural Terminology codes. In both HIV-positive and -negative cohorts, patient demographics, procedure-related complications, and postoperative revisions were recorded. Shapiro-Wilk, chi-square, Wilcoxon-Mann-Whitney, and multivariable logistic regression tests were used for statistical analysis. Results:. Of 173,421 patients who underwent breast reconstruction, 1816 had an HIV diagnosis. HIV-positive patients were younger (P < 0.001), underwent surgery more recently (P < 0.001), more often underwent immediate breast reconstruction (P < 0.001), and had higher comorbidity levels (P < 0.001). There was a regional variation in which the patient cohorts underwent breast reconstruction. There was no significant difference in overall complication rates between patient groups, but HIV-negative patients more often underwent revision procedures (P = 0.009). Conclusions:. When compared to their HIV-negative counterparts, breast reconstruction can be considered safe and efficacious in patients living with HIV. HIV-positive patients are a growing demographic who seek breast reconstruction, and surgeons must continue to further understand the unique implications of breast reconstruction in this population.

Objective: Medical decision-making is often uncertain. The positive predictive value (PPV) and negative predictive value (NPV) are conditional probabilities characterizing diagnostic tests and assessing diagnostic interventions in clinical medicine and epidemiology. The PPV is the probability that a patient has a specified disease, given a positive test result for that disease. The NPV is the probability that a patient does not have the disease, given a negative test result for that disease. Both values depend on disease incidence or prevalence, which may be highly uncertain for unfamiliar diseases, epidemics, etc. Probability distributions for this uncertainty are usually unavailable. We develop a non-probabilistic method for interpreting PPV and NPV with uncertain prevalence. Methods: Uncertainty in PPV and NPV is managed with the non-probabilistic concept of robustness in info-gap theory. Robustness of PPV or NPV estimates is the greatest uncertainty (in prevalence) at which the estimate’s error is acceptable. Results: Four properties are demonstrated. Zeroing: best estimates of PPV or NPV have no robustness to uncertain prevalence; best estimates are unreliable for interpreting diagnostic tests. Trade-off: robustness increases as error increases; this trade-off identifies robustly reliable error in PPV or NPV. Preference reversal: sometimes sub-optimal PPV or NPV estimates are more robust to uncertain incidence or prevalence than optimal estimates, motivating reversal of preference from the putative optimum to the sub-optimal estimate. Trade-off between specificity and robustness to uncertainty: the robustness increases as test-specificity decreases. These four properties underlie the interpretation of PPV and NPV. Conclusions: The PPV and NPV assess diagnostic tests, but are sensitive to lack of knowledge that generates non-probabilistic uncertain prevalence and must be supplemented with robustness analysis. When uncertainties abound, as with unfamiliar diseases, assessing robustness is critical to avoiding erroneous decisions.

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Abstract Investigations of abiotic and biotic contributions to dissolved organic carbon (DOC) are required to constrain microbial habitability in continental subsurface fluids. Here we investigate a large (101–283 mg C/L) DOC pool in an ancient (>1Ga), high temperature (45–55 °C), low biomass (102−104 cells/mL), and deep (3.2 km) brine from an uranium-enriched South African gold mine. Excitation-emission matrices (EEMs), negative electrospray ionization (–ESI) 21 tesla Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and amino acid analyses suggest the brine DOC is primarily radiolytically oxidized kerogen-rich shales or reefs, methane and ethane, with trace amounts of C3–C6 hydrocarbons and organic sulfides. δ2H and δ13C of C1–C3 hydrocarbons are consistent with abiotic origins. These findings suggest water-rock processes control redox and C cycling, helping support a meagre, slow biosphere over geologic time. A radiolytic-driven, habitable brine may signal similar settings are good targets in the search for life beyond Earth.

Abstract Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM.

Multiplex immunofluorescence (mIF) allows simultaneous antibody-based detection of multiple markers with a nuclear counterstain on a single tissue section. Recent studies have demonstrated that mIF is becoming an important tool for immune profiling the tumor microenvironment, further advancing our understanding of the interplay between cancer and the immune system, and identifying predictive biomarkers of response to immunotherapy. Expediting mIF discoveries is leading to improved diagnostic panels, whereas it is important that mIF protocols be standardized to facilitate their transition into clinical use. Manual processing of sections for mIF is time consuming and a potential source of variability across numerous samples. To increase reproducibility and throughput we demonstrate the use of an automated slide stainer for mIF incorporating tyramide signal amplification (TSA). We describe two panels aimed at characterizing the tumor immune microenvironment. Panel 1 included CD3, CD20, CD117, FOXP3, Ki67, pancytokeratins (CK), and DAPI, and Panel 2 included CD3, CD8, CD68, PD-1, PD-L1, CK, and DAPI. Primary antibodies were first tested by standard immunohistochemistry and single-plex IF, then multiplex panels were developed and images were obtained using a Vectra 3.0 multispectral imaging system. Various methods for image analysis (identifying cell types, determining cell densities, characterizing cell-cell associations) are outlined. These mIF protocols will be invaluable tools for immune profiling the tumor microenvironment.

Background:. Infection after implant-based breast reconstruction adversely affects surgical outcomes and increases healthcare utilization. This study aimed to quantify how postimplant breast reconstruction infections impact unplanned reoperations, hospital length of stay, and discontinuation of initially desired breast reconstruction. Methods:. We conducted a retrospective cohort study using Optum’s de-identifed Clinformatics Data Mart Database to analyze women undergoing implant breast reconstruction from 2003 to 2019. Unplanned reoperations were identified via Current Procedural Terminology (CPT) codes. Outcomes were analyzed via multivariate linear regression with Poisson distribution to determine statistical significance at P < 0.00625 (Bonferroni correction). Results:. In our national claims-based dataset, post-IBR infection rate was 8.53%. Subsequently, 31.2% patients had an implant removed, 6.9% had an implant replaced, 3.6% underwent autologous salvage, and 20.7% discontinued further reconstruction. Patients with a postoperative infection were significantly associated with increased incidence rate of total reoperations (IRR, 3.11; 95% CI, 2.92–3.31; P < 0.001) and total hospital length of stay (IRR, 1.55; 95% CI, 1.48–1.63; P < 0.001). Postoperative infections were associated with significantly increased odds of abandoning reconstruction (OR, 2.92; 95% CI, 0.081–0.11; P < 0.001). Conclusions:. Unplanned reoperations impact patients and healthcare systems. This national, claims-level study shows that post-IBR infection was associated with a 3.11× and 1.55× increase in the incidence rate of unplanned reoperations and length of stay. Post-IBR infection was associated with 2.92× increased odds of abandoning further reconstruction after implant removal.