Immunotherapeutic implications of negative regulation by protein tyrosine phosphatases in T cells: the emerging cases of PTP1B and TCPTP.

In the context of inflammation, T cell activation occurs by the concerted signals of the T cell receptor (TCR), co-stimulatory receptors ligation, and a pro-inflammatory cytokine microenvironment. Fine-tuning these signals is crucial to maintain T cell homeostasis and prevent self-reactivity while offering protection against infectious diseases and cancer. Recent developments in understanding the complex crosstalk between the molecular events controlling T cell activation and the balancing regulatory cues offer novel approaches for the development of T cell-based immunotherapies. Among the complex regulatory processes, the balance between protein tyrosine kinases (PTK) and the protein tyrosine phosphatases (PTPs) controls the transcriptional and metabolic programs that determine T cell function, fate decision, and activation. In those, PTPs are de facto regulators of signaling in T cells acting for the most part as negative regulators of the canonical TCR pathway, costimulatory molecules such as CD28, and cytokine signaling. In this review, we examine the function of two close PTP homologs, PTP1B (PTPN1) and T-cell PTP (TCPTP; PTPN2), which have been recently identified as promising candidates for novel T-cell immunotherapeutic approaches. Herein, we focus on recent studies that examine the known contributions of these PTPs to T-cell development, homeostasis, and T-cell-mediated immunity. Additionally, we describe the signaling networks that underscored the ability of TCPTP and PTP1B, either individually and notably in combination, to attenuate TCR and JAK/STAT signals affecting T cell responses. Thus, we anticipate that uncovering the role of these two PTPs in T-cell biology may lead to new treatment strategies in the field of cancer immunotherapy. This review concludes by exploring the impacts and risks that pharmacological inhibition of these PTP enzymes offers as a therapeutic approach in T-cell-based immunotherapies.

Venous Thromboembolism in Children and Teenagers Admitted for Trauma: A 5-Year Nationwide Perspective.

Venous thromboembolism (VTE) in pediatric trauma patients is under-investigated. The purpose of this study was to perform an evaluation of the risk factors for VTE after pediatric trauma, including readmissions across the United States. The Nationwide Readmissions Database for 2016-2020 was queried for all patients under the age of 18 years admitted for trauma. 276 670 patients were identified; 2063 (.8%) were diagnosed with VTE. Among those with VTE, 300 (15%) were identified during a readmission. Higher rates of VTE were seen in ages 15-17 years (n = 1,294, 1.3%, P < .001), penetrating injuries (n = 478, .9%, P < .001), and assault (n = 271, 2.7%, P < .001). The strongest risk factor for VTE was prolonged mechanical ventilation (OR 5.5 [4.9-6.3] P < .001). Our study found that a significant portion of post-traumatic VTE in children and teenagers occur during readmissions. A deeper understanding of the risk factors outlined here can guide enhanced clinical protocols, ensuring early detection and prevention of this complication.

Nationwide Propensity-Matched Comparison of Surgical Site Infections in Safety Net Hospitals.

This cohort study examines the rates and risks associated with surgical site infection during admission or readmission of socioeconomically marginalized patients undergoing gastrointestinal surgery.

AVDOS-VR: Affective Video Database with Physiological Signals and Continuous Ratings Collected Remotely in VR.

Investigating emotions relies on pre-validated stimuli to evaluate induced responses through subjective self-ratings and physiological changes. The creation of precise affect models necessitates extensive datasets. While datasets related to pictures, words, and sounds are abundant, those associated with videos are comparatively scarce. To overcome this challenge, we present the first virtual reality (VR) database with continuous self-ratings and physiological measures, including facial EMG. Videos were rated online using a head-mounted VR device (HMD) with attached emteqPRO mask and a cinema VR environment in remote home and laboratory settings with minimal setup requirements. This led to an affective video database with continuous valence and arousal self-rating measures and physiological responses (PPG, facial-EMG (7x), IMU). The AVDOS-VR database includes data from 37 participants who watched 30 randomly ordered videos (10 positive, neutral, and negative). Each 30-second video was assessed with two-minute relaxation between categories. Validation results suggest that remote data collection is ecologically valid, providing an effective strategy for future affective study designs. All data can be accessed via: www.gnacek.com/affective-video-database-online-study .

Inadvertent laparoscopic lavage of perforated colon cancer: a systematic review.

BACKGROUND: Although laparoscopic lavage for perforated diverticulitis with peritonitis has been grabbing the headlines, it is known that the clinical presentation of peritonitis can also be caused by an underlying perforated carcinoma. The aim of this study was to determine the incidence of patients undergoing inadvertent laparoscopic lavage of perforated colon cancer as well as the delay in cancer diagnosis. METHODS: The PubMed database was systematically searched to include all studies meeting inclusion criteria. Studies were screened through titles and abstracts with potentially eligible studies undergoing full-text screening. The primary endpoints of this meta-analysis were the rates of perforated colon cancer patients having undergone inadvertent laparoscopic lavage as well as the delay in cancer diagnosis. This was expressed in pooled rate % and 95% confidence intervals. RESULTS: Eleven studies (three randomized, two prospective, six retrospective) totaling 642 patients met inclusion criteria. Eight studies reported how patients were screened for cancer and the number of patients who completed follow-up. The pooled cancer rate was 3.4% (0.9%, 5.8%) with low heterogeneity (Isquare2 = 34.02%) in eight studies. Cancer rates were 8.2% (0%, 3%) (Isquare2 = 58.2%) and 1.7% (0%, 4.5%) (Isquare2 = 0%) in prospective and retrospective studies, respectively. Randomized trials reported a cancer rate of 7.2% (3.1%, 11.2%) with low among-study heterogeneity (Isquare2 = 0%) and a median delay to diagnosis of 2 (1.5-5) months. CONCLUSIONS: This systematic review found that 7% of patients undergoing laparoscopic lavage for peritonitis had perforated colon cancer with a delay to diagnosis of up to 5 months.

The induction of SHP-1 degradation by TAOK3 ensures the responsiveness of T cells to TCR stimulation.

Thousand-and-one-amino acid kinase 3 (TAOK3) is a serine and threonine kinase that belongs to the STE-20 family of kinases. Its absence reduces T cell receptor (TCR) signaling and increases the interaction of the tyrosine phosphatase SHP-1, a major negative regulator of proximal TCR signaling, with the kinase LCK, a component of the core TCR signaling complex. Here, we used mouse models and human cell lines to investigate the mechanism by which TAOK3 limits the interaction of SHP-1 with LCK. The loss of TAOK3 decreased the survival of naïve CD4+ T cells by dampening the transmission of tonic and ligand-dependent TCR signaling. In mouse T cells, Taok3 promoted the secretion of interleukin-2 (IL-2) in response to TCR activation in a manner that depended on Taok3 gene dosage and on Taok3 kinase activity. TCR desensitization in Taok3-/- T cells was caused by an increased abundance of Shp-1, and pharmacological inhibition of Shp-1 rescued the activation potential of these T cells. TAOK3 phosphorylated threonine-394 in the phosphatase domain of SHP-1, which promoted its ubiquitylation and proteasomal degradation. The loss of TAOK3 had no effect on the abundance of SHP-2, which lacks a residue corresponding to SHP-1 threonine-394. Modulation of SHP-1 abundance by TAOK3 thus serves as a rheostat for TCR signaling and determines the activation threshold of T lymphocytes.

Trends in Opioid Use Disorder Outpatient Treatment and Telehealth Utilization Before and During the COVID-19 Pandemic.

OBJECTIVE: The authors examined trends in opioid use disorder treatment and in-person and telehealth modalities before and after COVID-19 pandemic onset among patients who had received treatment prepandemic. METHODS: The sample included 13,113 adults with commercial insurance or Medicare Advantage and receiving opioid use disorder treatment between March 2018 and February 2019. Trends in opioid use disorder outpatient treatment, treatment with medications for opioid use disorder (MOUD), and in-person and telehealth modalities were examined 1 year before pandemic onset and 2 years after (March 2019-February 2022). RESULTS: From March 2019 to February 2022, the proportion of patients with opioid use disorder outpatient and MOUD visits declined by 2.8 and 0.3 percentage points, respectively. Prepandemic, 98.6% of outpatient visits were in person; after pandemic onset, at least 34.9% of patients received outpatient care via telehealth. CONCLUSIONS: Disruptions in opioid use disorder outpatient and MOUD treatments were marginal during the pandemic, possibly because of increased telehealth utilization.

Methodology for Good Machine Learning with Multi-Omics Data.

In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.

Using machine learning to improve anaphylaxis case identification in medical claims data.

Objective: Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes. Methods: This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets. Then a variety of unsupervised and supervised methods were used (eg, Sammon mapping and eXtreme Gradient Boosting) to train models on datasets of differing data quality, which reflects the varying availability and potential rarity of ground truth data in medical databases. Results: Resulting machine learning model accuracies ranged between 47.7% and 94.4% when tested on ground truth data. Finally, we found new features to help experts enhance existing case-finding algorithms. Discussion: Developing precise algorithms to detect medical outcomes in claims can be a laborious and expensive process, particularly for conditions presented and coded diversely. We found it beneficial to filter out highly potent codes used for data curation to identify underlying patterns and features. To improve rule-based algorithms where necessary, researchers could use model explainers to determine noteworthy features, which could then be shared with experts and included in the algorithm. Conclusion: Our work suggests machine learning models can perform at similar levels as a previously published expert case-finding algorithm, while also having the potential to improve performance or streamline algorithm construction processes by identifying new relevant features for algorithm construction.

Clinical assessment of the fetal right Quantitative Lung Index.

OBJECTIVES: We have previously described gestational-age-independent sonographic indices to assess fetal lung size in the right and left lungs: The Quantitative Lung Index for the right lung (QLI-R) and for the left lung (QLI-L), respectively. The purpose of this study was to evaluate the clinical cutoff point of the QLI-R to predict pulmonary hypoplasia and neonatal death. MATERIALS AND METHODS: Retrospective assessment of the QLI-R in patients with left-sided congenital diaphragmatic hernia (CDH-L) and other fetal conditions at risk for fetal pulmonary hypoplasia. Cross-section and longitudinal assessment of the behavior of the QLI-R in untreated and treated patients. ROC curve analysis to determine the optimal cutoff point of the QLI-R in predicting neonatal death. RESULTS: One hundred eighteen patients with CDH-L and other fetal conditions at risk for pulmonary hypoplasia had QLI-R measurements done. Seventeen patients were excluded for various reasons. Eleven patients with conditions other than CDH-L but at risk for pulmonary hypoplasia were used for intraclass coefficient measurements of the QLI-R. Ninety patients had CDH-L, of which 78 did not undergo antenatal intervention and in which the cutoff point for pulmonary hypoplasia and neonatal demise was assessed. Stent tracheal occlusion was performed in the remaining 12 patients with CDH-L, in which the behavior of the QLI after surgery was assessed. Analysis of the ICC showed an overall intra-rater reliability of 0.985 (Cronbach's Alpha-based). There was no correlation between gestational age and QLI-R (-0.73, Pearson correlation, p = .72). Twenty-six of the 78 patients (33%) with CDH-L managed expectantly had a neonatal demise. A QLI-R equal to or less than 0.45 was significantly predictive of neonatal demise (area under the curve 0.64, p = .046, sensitivity 77%). Nine of the 12 patients (75%) that underwent tracheal occlusion had neonatal survival. Of these, 10 had serial assessments of the QLI-R after surgery. An increase in the QLI-R of 0.11 was associated with a tendency for neonatal survival (p = .056). CONCLUSION: Our study confirms that the QLI-R is a gestational-age-independent measurement of fetal lung size, with a high degree of reproducibility. In a population of expectantly managed CDH-L patients, a cutoff value of the QLI-R of 0.45 or lower is predictive of neonatal death from pulmonary hypoplasia. The QLI-R can be used to monitor fetal lung growth after tracheal occlusion, and an increase in the QLI-R is suggestive of neonatal survival. Further prospective studies are needed to confirm these findings and to explore the use of the QLI in other populations at risk for pulmonary hypoplasia and consequent neonatal demise.

Comparing Severely Injured Trauma Patients Admitted to Investor-Owned versus Public and Not-For-Profit Hospitals Reveals Opportunities for Improvement in the US.

BACKGROUND: Non-elderly trauma patients represent the largest portion of preventable years of life loss in the US. The purpose of this study was to compare outcomes in patients admitted to investor-owned vs public and not-for-profit hospitals across the US. MATERIAL AND METHODS: The Nationwide Readmissions Database 2018 was queried for trauma patients with an Injury Severity Score greater than 15 and age 18-65 years. The primary outcome was mortality; secondary outcomes were prolonged length of stay (LOS) greater than 30 days, readmission within 30 days, and readmission to a different hospital. Patients admitted to investor-owned hospitals were compared to public and not-for-profit hospitals. Univariable analysis was performed using chi-squared tests. Multivariable logistic regression was performed for each outcome. RESULTS: 157 945 patients were included with 11.0% (n = 17 346) admitted to investor-owned hospitals. The overall mortality rate and prolonged LOS were similar for both groups. The overall readmission rate was 9.2% (n = 13 895), with the rate in investor-owned hospitals at 10.5% (n = 1,739, P < .001). Multivariable logistic regression revealed investor-owned hospitals had an increased risk of readmission (OR 1.2 [1.1-1.3] P < .001) and readmission to a different hospital (OR 1.3 [1.2-1.5] P < .001). DISCUSSION: Severely injured trauma patients have similar rates of mortality and prolonged length of stay in investor-owned vs public and not-for-profit hospitals. However, patients admitted to investor-owned hospitals have an increased risk of readmission and readmission to different hospitals. Efforts to improve outcomes after trauma must consider hospital ownership and readmission to different hospitals.

Comparing Payments to Surgeons From Drug and Medical Device Corporations Reveals Inequalities Between Genders and Specialties.

INTRODUCTION: Maintaining trust in the patient-doctor relationship requires transparency in the details of the financial relationships between physicians and drug and medical device corporations. These details are publicly available through the Open Payments database, and patients are encouraged to ask surgeons to interpret their implications. The purpose of this study was to better equip surgeons in responding to these inquiries and to compare the distribution of these payments by gender and specialty. METHODS: The 2021 Open Payments dataset was searched for all payments to surgeons from the 14 different specialties recognized by the American College of Surgeons. The total payments per surgeon were compared by calculating the mean and median payments. The Gini index, a measure of income inequality, was also calculated for each specialty. RESULTS: There were 96 724 surgeons who received over $755 million in payments from drug and medical device companies. There were 72 245 (74.7%) men and 24 479 (25.3%) women. The total amount of payments to men was $712 million (94.2%) and for women it was $44 million (5.8%). The overall Gini index was .9508. The specialty with the highest Gini index was pediatric surgery (.9844) and the lowest was cardiothoracic surgery (.8656). DISCUSSION: Male surgeons received disproportionately higher payments from drug and device corporations than female surgeons. Surgeons should be aware of their own standing within the Open Payments database in order to respond appropriately to patient inquiries.

Association of hypoglossal nerve stimulator response with machine learning identified negative effort dependence patterns.

BACKGROUND: Hypoglossal nerve stimulator (HGNS) is a therapeutic option for moderate to severe obstructive sleep apnea (OSA). Improved patient selection criteria are needed to target those most likely to benefit. We hypothesized that the pattern of negative effort dependence (NED) on inspiratory flow limited waveforms recorded during sleep, which has been correlated with the site of upper airway collapse, would contribute to the prediction of HGNS outcome. We developed a machine learning (ML) algorithm to identify NED patterns in pre-treatment sleep studies. We hypothesized that the predominant NED pattern would differ between HGNS responders and non-responders. METHODS: An ML algorithm to identify NED patterns on the inspiratory portion of the nasal pressure waveform was derived from 5 development set polysomnograms. The algorithm was applied to pre-treatment sleep studies of subjects who underwent HGNS implantation to determine the percentage of each NED pattern. HGNS response was defined by STAR trial criteria for success (apnea-hypopnea index (AHI) reduced by > 50% and < 20/h) as well as by a change in AHI and oxygenation metrics. The predominant NED pattern in HGNS responders and non-responders was determined. Other variables including demographics and oxygenation metrics were also assessed between responders and non-responders. RESULTS: Of 45 subjects, 4 were excluded due to technically inadequate polysomnograms. In the remaining 41 subjects, ML accurately distinguished three NED patterns (minimal, non-discontinuous, and discontinuous). The percentage of NED minimal breaths was significantly greater in responders compared with non-responders (p = 0.01) when the response was defined based on STAR trial criteria, change in AHI, and oxygenation metrics. CONCLUSION: ML can accurately identify NED patterns in pre-treatment sleep studies. There was a statistically significant difference in the predominant NED pattern between HGNS responders and non-responders with a greater NED minimal pattern in responders. Prospective studies incorporating NED patterns into predictive modeling of factors determining HGNS outcomes are needed.

Randomized Open Investigation Determining Steroid Dose in Severe COVID-19: The ROIDS-Dose Clinical Trial.

Introduction Treatment with dexamethasone reduces mortality in patients with coronavirus disease 2019 (COVID-19) pneumonia requiring supplemental oxygen, but the optimal dose has not been determined. Objective To determine whether weight-based dexamethasone of 0.2 mg/kg is superior to 6 mg daily in reducing 28-day mortality in patients with COVID-19 and hypoxemia. Materials and methods A multicenter, open-label, randomized clinical trial was conducted between March 2021 and December 2021 at seven hospitals within Northwell Health. A total of 142 patients with confirmed COVID-19 and hypoxemia were included. Participants were randomized in a 1:1 ratio to dexamethasone 0.2 mg/kg intravenously daily (n = 70) or 6 mg daily (n = 72) for up to 10 days. Results There was no statistically significant difference in the primary outcome of 28-day all-cause mortality with deaths in 12 of 70 patients (17.14%) in the intervention group and 15 of 72 patients (20.83%) in the control group (p = 0.58). There were no statistically significant differences among the secondary outcomes. Conclusion In patients with COVID-19 and hypoxemia, the use of weight-based dexamethasone dosing was not superior to dexamethasone 6 mg in reducing all-cause mortality at 28 days. Clinical trial registration This study was registered under ClinicalTrials.gov (identifier: NCT04834375).

A Ferrofluid with High Specific Absorption Rate Prepared in a Single Step Using a Biopolymer.

An exhaustive characterization of the physicochemical properties of gum arabic (GA)-coated Fe3O4 magnetic nanoparticles was conducted in this work. These nanoparticles were obtained via the in-situ coprecipitation method (a fast single-step method) in two GA:Fe ratios, 10:1 and 20:1, respectively. Several experimental techniques were applied in the characterization process, all of them described below. Using Transmission Electron Microcopy images, they were shown to have spherical-like morphology with 11 nm diameter. The Fourier Transform Infrared spectra confirmed the attachment of the GA on the surface of the magnetic nanoparticles (MNPs), providing good colloidal stability from pH 7 to 8. The thickness of the coatings (1.7 nm and 1.1 nm) was determined using thermogravimetric measurements. A high specific absorption rate and superparamagnetic properties were determined using alternant and static magnetic fields, respectively. The GA-coated MNPs were non-cytotoxic, according to tests on HT-29 human intestine cells. Additionally, HT-29 cells were exposed to magnetic fluid hyperthermia at 530 kHz, and the induction of cell death by the magnetic field, due to the heating of GA-coated MNP, was observed.

A Ferrofluid with Surface Modified Nanoparticles for Magnetic Hyperthermia and High ROS Production.

A ferrofluid with 1,2-Benzenediol-coated iron oxide nanoparticles was synthesized and physicochemically analyzed. This colloidal system was prepared following the typical co-precipitation method, and superparamagnetic nanoparticles of 13.5 nm average diameter, 34 emu/g of magnetic saturation, and 285 K of blocking temperature were obtained. Additionally, the zeta potential showed a suitable colloidal stability for cancer therapy assays and the magneto-calorimetric trails determined a high power absorption density. In addition, the oxidative capability of the ferrofluid was corroborated by performing the Fenton reaction with methylene blue (MB) dissolved in water, where the ferrofluid was suitable for producing reactive oxygen species (ROS), and surprisingly a strong degradation of MB was also observed when it was combined with H2O2. The intracellular ROS production was qualitatively corroborated using the HT-29 human cell line, by detecting the fluorescent rise induced in 2,7-dichlorofluorescein diacetate. In other experiments, cell metabolic activity was measured, and no toxicity was observed, even with concentrations of up to 4 mg/mL of magnetic nanoparticles (MNPs). When the cells were treated with magnetic hyperthermia, 80% of cells were dead at 43 °C using 3 mg/mL of MNPs and applying a magnetic field of 530 kHz with 20 kA/m amplitude.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. RESULTS: In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. CONCLUSIONS: The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

The quantitative lung index: the left lung.

OBJECTIVE: We have previously described a gestational age-independent sonographic parameter to assess fetal lung growth in the right lung (right quantitative lung index, or QLI-R). The purpose of this study was to develop a similar sonographic parameter to assess the growth of the left lung in the fetus, independent of gestational age, or QLI-L. STUDY DESIGN: A new index, the QLI-L was derived using published formulas for the head circumference (HC) and the area of the base of the left lung (LA), with the corresponding percentiles. RESULTS: Left lung growth can be expressed using the following formula: QLI-L=LAL(HC12)2. The 50th percentile of the QLI-L remained approximately constant at 1.0 for the GA between 16-32 weeks. A small left lung (<1st percentile) was defined as a QLI-L < 0.5. CONCLUSIONS: Fetal left lung growth can be adequately described independent of gestational age using the QLI-L. Further experience is needed to assess the clinical accuracy of the QLI-L in characterizing fetal left lung growth.

Neuroendocrine neoplasms of the gallbladder: early detection and surgery is key to improved outcome.

PURPOSE: Neuroendocrine neoplasms (NENs) of the gallbladder are very rare. As a result, the classification of pathologic specimens from gallbladder NENs, currently classified as gallbladder neuroendocrine tumors (GB-NETs) and carcinomas (GB-NECs), is inconsistent and makes nomenclature, classification, and management difficult. Our study aims to evaluate the epidemiological trend, tumor biology, and outcomes of GB-NET and GB-NEC over the last 5 decades. METHODS: This is a retrospective analysis of the SEER database from 1973 to 2016. The epidemiological trend was analyzed using the age-adjusted Joinpoint regression analysis. Survival was assessed with Kaplan-Meier analysis and Cox regression was used to assess predictors of poor survival. RESULTS: A total of 482 patients with GB-NEN were identified. Mean age at diagnosis was 65.2 ± 14.3 years. Females outnumbered males (65.6% vs. 34.4%). The Joinpoint nationwide trend analysis showed a 7% increase per year from 1973 to 2016. The mean survival time after diagnosis of GB-NEN was 37.11 ± 55.3 months. The most common pattern of nodal distribution was N0 (50.2%) followed by N1 (30.9%) and N2 (19.2%). Advanced tumor spread (into the liver, regional, and distant metastasis) was seen in 60.3% of patients. Patients who underwent surgery had a significant survival advantage (111.0 ± 8.3 vs. 8.3 ± 1.2 months, p < 0.01). Cox regression analysis showed advanced age (p < 0.01), tumor stage (P < 0.01), tumor extension (p < 0.01), and histopathologic grade (p < 0.01) were associated with higher mortality. CONCLUSION: Gallbladder NENs are a rare histopathological variant of gallbladder cancer that is showing a rising incidence in the USA. In addition to tumor staging, surgical resection significantly impacts patient survival, when patients are able to undergo surgery irrespective of tumor staging. Advanced age, tumor extension, and histopathological grade of the tumor were associated with higher mortality.

Studying how state health services delivery policies can mitigate the effects of disasters on drug addiction treatment and overdose: Protocol for a mixed-methods study.

BACKGROUND: The United States is experiencing a drug addiction and overdose crisis, made worse by the COVID-19 pandemic. Relative to other types of health services, addiction treatment and overdose prevention services are particularly vulnerable to disaster-related disruptions for multiple reasons including fragmentation from the general medical system and stigma, which may lead decisionmakers and providers to de-prioritize these services during disasters. In response to the COVID-19 pandemic, U.S. states implemented multiple policies designed to mitigate disruptions to addiction treatment and overdose prevention services, for example policies expanding access to addiction treatment delivered via telehealth and policies designed to support continuity of naloxone distribution programs. There is limited evidence on the effects of these policies on addiction treatment and overdose. This evidence is needed to inform state policy design in future disasters, as well as to inform decisions regarding whether to sustain these policies post-pandemic. METHODS: The overall study uses a concurrent-embedded design. Aims 1-2 use difference-in-differences analyses of large-scale observational databases to examine how state policies designed to mitigate the effects of the COVID-19 pandemic on health services delivery influenced addiction treatment delivery and overdose during the pandemic. Aim 3 uses a qualitative embedded multiple case study approach, in which we characterize local implementation of the state policies of interest; most public health disaster policies are enacted at the state level but implemented at the local level by healthcare systems and local public health authorities. DISCUSSION: Triangulation of results across methods will yield robust understanding of whether and how state disaster-response policies influenced drug addiction treatment and overdose during the COVID-19 pandemic. Results will inform policy enactment and implementation in future public health disasters. Results will also inform decisions about whether to sustain COVID-19 pandemic-related changes to policies governing delivery addiction and overdose prevention services long-term.