Esophageal Lichen Planus: The Efficacy and Safety of Tacrolimus in Reducing Inflammation and Need for Dilation.

BACKGROUND AND AIMS: Esophageal Lichen Planus (ELP) is a rare inflammatory disease most seen in middle-aged Caucasian women, manifested by sloughing mucosa, thick exudate, and proximal strictures. Most case reports and small series highlight using steroids and other immunosuppressants. To our knowledge, oral tablet tacrolimus has not been studied. We aimed to assess the change in ELP after oral tacrolimus treatment. METHODS: The primary outcome was the efficacy of tacrolimus objectively through our scoring system, ELPSS. All consecutive adults with ELP who underwent more than one EGD by two esophagologists and being treated with tacrolimus or other treatment were eligible for inclusion in this retrospective cohort study. Inflammation and fibrostenotic disease were graded using the novel ELP Severity Score (ELPSS). RESULTS: Twenty two patients met the inclusion criteria. Half (11) received tacrolimus (dose 1-2 mg BID) and half (11) received other therapy (i.e. cyclosporine, topical steroids, or none). Mean ELPSS on first EGD, extraesophageal manifestations of disease, presenting symptoms, and baseline characteristics were similar between groups. Among patients on Tac vs No-Tac, there was a statistically significant improvement in ELPSS (mean difference 1.8 pts; 95% CI 0.25-3.38; P=0.02). Response rate was 89% with Tac vs 30% with No-Tac (P=0.025). All 22 patients underwent bougie dilation safely with a mean diameter of 16 mm achieved. Patients on Tac also required less frequent dilation. CONCLUSION: Oral tablet tacrolimus reduced the inflammatory and fibrostenotic components of ELP. Thus, low-dose oral tacrolimus is safe and should be considered in patients with more severe disease.

Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response.

BACKGROUND: Months after infection with severe acute respiratory syndrome coronavirus 2, at least 10% of patients still experience complaints. Long-COVID (coronavirus disease 2019) is a heterogeneous disease, and clustering efforts revealed multiple phenotypes on a clinical level. However, the molecular pathways underlying long-COVID phenotypes are still poorly understood. OBJECTIVES: We sought to cluster patients according to their blood transcriptomes and uncover the pathways underlying their disease. METHODS: Blood was collected from 77 patients with long-COVID from the Precision Medicine for more Oxygen (P4O2) COVID-19 study. Unsupervised hierarchical clustering was performed on the whole blood transcriptome. These clusters were analyzed for differences in clinical features, pulmonary function tests, and gene ontology term enrichment. RESULTS: Clustering revealed 2 distinct clusters on a transcriptome level. Compared with cluster 2 (n = 65), patients in cluster 1 (n = 12) showed a higher rate of preexisting cardiovascular disease (58% vs 22%), higher prevalence of gastrointestinal symptoms (58% vs 29%), shorter hospital duration during severe acute respiratory syndrome coronavirus 2 infection (median, 3 vs 8 days), lower FEV1/forced vital capacity (72% vs 81%), and lower diffusion capacity of the lung for carbon monoxide (68% vs 85% predicted). Gene ontology term enrichment analysis revealed upregulation of genes involved in the antiviral innate immune response in cluster 1, whereas genes involved with the adaptive immune response were upregulated in cluster 2. CONCLUSIONS: This study provides a start in uncovering the pathophysiological mechanisms underlying long-COVID. Further research is required to unravel why the immune response is different in these clusters, and to identify potential therapeutic targets to create an optimized treatment or monitoring strategy for the individual long-COVID patient.

Long COVID exhibits clinically distinct phenotypes at 3-6 months post-SARS-CoV-2 infection: results from the P4O2 consortium.

BACKGROUND: Four months after SARS-CoV-2 infection, 22%-50% of COVID-19 patients still experience complaints. Long COVID is a heterogeneous disease and finding subtypes could aid in optimising and developing treatment for the individual patient. METHODS: Data were collected from 95 patients in the P4O2 COVID-19 cohort at 3-6 months after infection. Unsupervised hierarchical clustering was performed on patient characteristics, characteristics from acute SARS-CoV-2 infection, long COVID symptom data, lung function and questionnaires describing the impact and severity of long COVID. To assess robustness, partitioning around medoids was used as alternative clustering. RESULTS: Three distinct clusters of patients with long COVID were revealed. Cluster 1 (44%) represented predominantly female patients (93%) with pre-existing asthma and suffered from a median of four symptom categories, including fatigue and respiratory and neurological symptoms. They showed a milder SARS-CoV-2 infection. Cluster 2 (38%) consisted of predominantly male patients (83%) with cardiovascular disease (CVD) and suffered from a median of three symptom categories, most commonly respiratory and neurological symptoms. This cluster also showed a significantly lower forced expiratory volume within 1 s and diffusion capacity of the lung for carbon monoxide. Cluster 3 (18%) was predominantly male (88%) with pre-existing CVD and diabetes. This cluster showed the mildest long COVID, and suffered from symptoms in a median of one symptom category. CONCLUSIONS: Long COVID patients can be clustered into three distinct phenotypes based on their clinical presentation and easily obtainable information. These clusters show distinction in patient characteristics, lung function, long COVID severity and acute SARS-CoV-2 infection severity. This clustering can help in selecting the most beneficial monitoring and/or treatment strategies for patients suffering from long COVID. Follow-up research is needed to reveal the underlying molecular mechanisms implicated in the different phenotypes and determine the efficacy of treatment.

Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study.

BACKGROUND: Artificial intelligence (AI)-powered technologies are being increasingly used in almost all fields, including medicine. However, to successfully implement medical AI applications, ensuring trust and acceptance toward such technologies is crucial for their successful spread and timely adoption worldwide. Although AI applications in medicine provide advantages to the current health care system, there are also various associated challenges regarding, for instance, data privacy, accountability, and equity and fairness, which could hinder medical AI application implementation. OBJECTIVE: The aim of this study was to identify factors related to trust in and acceptance of novel AI-powered medical technologies and to assess the relevance of those factors among relevant stakeholders. METHODS: This study used a mixed methods design. First, a rapid review of the existing literature was conducted, aiming to identify various factors related to trust in and acceptance of novel AI applications in medicine. Next, an electronic survey including the rapid review-derived factors was disseminated among key stakeholder groups. Participants (N=22) were asked to assess on a 5-point Likert scale (1=irrelevant to 5=relevant) to what extent they thought the various factors (N=19) were relevant to trust in and acceptance of novel AI applications in medicine. RESULTS: The rapid review (N=32 papers) yielded 110 factors related to trust and 77 factors related to acceptance toward AI technology in medicine. Closely related factors were assigned to 1 of the 19 overarching umbrella factors, which were further grouped into 4 categories: human-related (ie, the type of institution AI professionals originate from), technology-related (ie, the explainability and transparency of AI application processes and outcomes), ethical and legal (ie, data use transparency), and additional factors (ie, AI applications being environment friendly). The categorized 19 umbrella factors were presented as survey statements, which were evaluated by relevant stakeholders. Survey participants (N=22) represented researchers (n=18, 82%), technology providers (n=5, 23%), hospital staff (n=3, 14%), and policy makers (n=3, 14%). Of the 19 factors, 16 (84%) human-related, technology-related, ethical and legal, and additional factors were considered to be of high relevance to trust in and acceptance of novel AI applications in medicine. The patient's gender, age, and education level were found to be of low relevance (3/19, 16%). CONCLUSIONS: The results of this study could help the implementers of medical AI applications to understand what drives trust and acceptance toward AI-powered technologies among key stakeholders in medicine. Consequently, this would allow the implementers to identify strategies that facilitate trust in and acceptance of medical AI applications among key stakeholders and potential users.

Modeling the importance of fish condition, overall health, and disease on the fecundity of White Perch in the Choptank River.

OBJECTIVE: Modeling of fecundity with allometric, nutritional, and environmental covariates has increased sensitivity of reproductive metrics in many fish species. In estuaries with heavy anthropogenic influence, resident species often experience sublethal health impacts because of increased stress, which can include increases in gonadal pathology, intersex, or potential reproductive failure. This study models the fecundity of the estuarine species White Perch Morone americana in response to health parameters identified as signals of habitat stress, including gross pathology presentation, nutritional condition, and disease presence. METHODS: Subpopulation fecundity in the Choptank River (Maryland) of the Chesapeake Bay was estimated using stereological fecundity sampling methods and modeled using information-theoretic approaches of model selection. Nutritional and health parameters identified through health assessment techniques, specific somatic indices, and disease presence were selected as covariates. RESULT: Nutrition demonstrated limited influence on model fit as compared to models with only conventional allometric variables such as weight and length. Of the health variables, gross pathology and somatic indices showed minimal influence on selection, but mycobacterial infection, a chronic condition in the Chesapeake Bay among temperate basses, showed measurable influence. Models with mycobacteriosis included were 40 times more likely the best fit when compared to models with only allometric parameters. CONCLUSION: Whether this has a region-wide influence on all subpopulations will require further research and sampling of the magnitude of mycobacteriosis infection.

Precision Medicine for More Oxygen (P4O2)-Study Design and First Results of the Long COVID-19 Extension.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40-65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3-6 and 12-18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient's home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3-6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.

Sour Gas Adsorption on Silica Gels.

One of the essential factors for water adsorption on silica gels is the concentration of silanol groups on the silica surface. However, no systematic investigation on the adsorption of sour gas components, methane (CH4), carbon dioxide (CO2), and hydrogen sulfide (H2S) on silica gels with different textural properties and surface silanol concentrations, has been conducted. Three silica gels of 22, 30, and 60 Å pore sizes, with silanol concentrations of αtotal = 2.516, 2.340, and 2.152 OH nm-2, respectively, were studied in this work. The adsorption data for CH4, CO2, H2S, and H2O at T = 0, 25, and 50 °C on the 22 and 30 Å pore size silica gels were presented, and a comparison of the data for the 60 Å pore size silica gel on the same adsorbates was conducted. All three silica gels showed an adsorption affinity in the order of H2O > H2S > CO2 > CH4. The isosteric heats of adsorption of H2O and H2S had a greater dependence on the silanol concentration than CO2 and CH4. At p < 10 bar, there was no difference in the adsorption per m2 of CH4 between the silica gels (n ads = 1.7 mmol m-2, for all silicas at p = 10 bar), while higher pressures resulted in greater adsorption capacity in the larger pore volume silica gels (at p = 20 bar: n ads = 3.0, 3.3, and 3.4 mmol m-2 for the 22, 30, and 60 Å pore size silicas, respectively). H2S adsorption at low pressures (p < 4 bar) was larger on the samples with larger silanol concentrations (at p = 3 bar: n ads = 6.1, 4.7, and 4.5 mmol m-2 for the 22, 30, and 60 Å pore size silicas, respectively), but above p = 4 bar, the 60 Å pore size silica had a greater adsorption capacity than the 30 Å pore size (at p = 5 bar: n ads = 8.0, 6.0, and 6.2 mmol m-2 for the 22, 30, and 60 Å pore size silicas, respectively).

Lichen planus esophagitis.

PURPOSE OF REVIEW: Dysphagia is one of the most common reasons for patients' visits to a gastroenterologist. Esophageal lichen planus (ELP) has historically been felt to be a rare disease, when in fact it is often misdiagnosed and unrecognized. Often first diagnosed as an unusual esophagitis, all gastroenterologists will see ELP in their practice, and need to be able to recognize this condition. RECENT FINDINGS: Although there is still a relative paucity of data on this condition, this article will update the typical presenting symptoms, endoscopic findings, and ways to differentiate ELP from other inflammatory mucosal diseases. There is still no standardized treatment algorithm, but we will also present the most recent treatment approaches. SUMMARY: It is critical that physicians maintain an increased awareness of ELP and have a high clinical suspicion in the appropriate patients. While management remains challenging, it is important to treat both the inflammatory and stricturing components of the disease. A multidisciplinary approach is also often required, utilizing dermatologists, gynecologists, and dentists who are familiar with managing patients with LP.

Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility.

In the context of a continuously increasing human population that needs to be fed, with environmental protection in mind, nitrogen use efficiency (NUE) improvement is becoming very important. To understand the natural variation of traits linked to nitrogen uptake efficiency (UPE), one component of NUE, the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting nitrogen (N) levels in a high-throughput phenotyping facility for six weeks. Three biomass-related, three root-related, and two reflectance-related traits were measured weekly under each treatment. Subsequently, the population was genetically analysed using a total of 13,060 polymorphic haplotypes and singular SNPs for a genome-wide association study (GWAS). In total, we detected 543 quantitative trait loci (QTL) across all time points and traits, which were pooled into 42 stable QTL (sQTL; present in at least three of the six weeks). Besides Rht-B1 and Rht-D1, candidate genes playing a role in gibberellic acid-regulated growth and nitrate transporter genes from the NPF gene family, like NRT 1.1, were linked to sQTL. Two novel sQTL on chromosomes 5 A and 6D showed pleiotropic effects on several traits. The high number of N-specific sQTL indicates that selection for UPE is useful specifically under N-limited conditions.

Impact of opioids on treatment response among idiopathic esophagogastric junction outflow obstruction patients: A retrospective cohort study.

BACKGROUND: Esophageal dysmotility has been attributed to opioid use. The goal was to assess the differences in pre- and post-treatment timed-barium esophagram (TBE) barium heights at 1 and 5 minutes and symptomatic response to treatment in esophagogastric junction outflow obstruction (EGJOO) patients according to opioid use status. METHODS: We performed a retrospective cohort study. Consecutive patients with EGJOO were eligible for inclusion. Data were collected on demographics, pre and post-treatment 1 and 5 minutes TBE barium heights and symptom outcomes. Groups were compared according to opioid use. RESULTS: Thirty-one EGJOO patients met the inclusion criteria. All patients were treated with pneumatic dilation. Of the 31 patients, 11 (35%) had opioid exposure and 20 (65%) did not. The median follow-up post-treatment was two months (range 1-47 months). There was no statistically significant difference in post-treatment outcomes for opioid exposed vs. unexposed groups. The median per cent decrease in the TBE barium height at 1 minute was 100% for the opioid exposed vs. 71% for the unexposed group (p = 0.92). The median per cent decrease in the TBE barium height at 5 minutes was zero % for the opioid exposed and unexposed groups (p = 0.67). The incidence of symptomatic improvement was 82% (9/11) for the opioid exposed group vs 95% (19/20) for the unexposed group (p = 0.28). CONCLUSIONS: Patients with EGJOO seem to respond to treatment similarly regardless of being on opioids.

Longer epidermal cells underlie a quantitative source of variation in wheat flag leaf size.

The wheat flag leaf is the main contributor of photosynthetic assimilates to developing grains. Understanding how canopy architecture strategies affect source strength and yield will aid improved crop design. We used an eight-founder population to investigate the genetic architecture of flag leaf area, length, width and angle in European wheat. For the strongest genetic locus identified, we subsequently created a near-isogenic line (NIL) pair for more detailed investigation across seven test environments. Genetic control of traits investigated was highly polygenic, with colocalisation of replicated quantitative trait loci (QTL) for one or more traits identifying 24 loci. For QTL QFll.niab-5A.1 (FLL5A), development of a NIL pair found the FLL5A+ allele commonly conferred a c. 7% increase in flag and second leaf length and a more erect leaf angle, resulting in higher flag and/or second leaf area. Increased FLL5A-mediated flag leaf length was associated with: (1) longer pavement cells and (2) larger stomata at lower density, with a trend for decreased maximum stomatal conductance (Gsmax ) per unit leaf area. For FLL5A, cell size rather than number predominantly determined leaf length. The observed trade-offs between leaf size and stomatal morphology highlight the need for future studies to consider these traits at the whole-leaf level.

Elaborating the potential of Artificial Intelligence in automated CAR-T cell manufacturing.

This paper discusses the challenges of producing CAR-T cells for cancer treatment and the potential for Artificial Intelligence (AI) for its improvement. CAR-T cell therapy was approved in 2018 as the first Advanced Therapy Medicinal Product (ATMP) for treating acute leukemia and lymphoma. ATMPs are cell- and gene-based therapies that show great promise for treating various cancers and hereditary diseases. While some new ATMPs have been approved, ongoing clinical trials are expected to lead to the approval of many more. However, the production of CAR-T cells presents a significant challenge due to the high costs associated with the manufacturing process, making the therapy very expensive (approx. $400,000). Furthermore, autologous CAR-T therapy is limited to a make-to-order approach, which makes scaling economical production difficult. First attempts are being made to automate this multi-step manufacturing process, which will not only directly reduce the high manufacturing costs but will also enable comprehensive data collection. AI technologies have the ability to analyze this data and convert it into knowledge and insights. In order to exploit these opportunities, this paper analyses the data potential in the automated CAR-T production process and creates a mapping to the capabilities of AI applications. The paper explores the possible use of AI in analyzing the data generated during the automated process and its capabilities to further improve the efficiency and cost-effectiveness of CAR-T cell production.

Growth Rates of Vibrio parahaemolyticus Sequence Type 36 Strains in Live Oysters and in Culture Medium.

The pathogenic marine bacterium Vibrio parahaemolyticus can cause seafood-related gastroenteritis via the consumption of raw or undercooked seafood. Infections originating from relatively cool waters in the northeast United States are typically rare, but recently, this region has shown an increase in infections attributed to the ecological introduction of pathogenic sequence type 36 (ST36) strains, which are endemic to the cool waters of the Pacific Northwest. A 2005 risk assessment performed by the Food and Drug Administration (FDA) modeled the postharvest growth of V. parahaemolyticus in oysters as a function of air temperature and the length of time the oysters remained unrefrigerated. This model, while useful, has raised questions about strain growth differences in oyster tissue and whether invasive pathogenic strains exhibit different growth rates than nonclinical strains, particularly at lower temperatures. To investigate this question, live eastern oysters were injected with ST36 clinical strains and non-ST36 nonclinical strains, and growth rates were measured using the most probable number (MPN) enumeration. The presence of V. parahaemolyticus was confirmed using PCR by targeting the thermolabile hemolysin gene (tlh), thermostable direct hemolysin (tdh), tdh-related hemolysin (trh), and a pathogenesis-related protein (prp). The growth rates of the ST36 strains were compared to the FDA model and several other data sets of V. parahaemolyticus growth in naturally inoculated oysters harvested from the Chesapeake Bay. Our data indicate that the growth rates from most studies fall within the mean of the FDA model, but with slightly higher growth at lower temperatures for ST36 strains injected into live oysters. These data suggest that further investigations of ST36 growth capability in oysters at temperatures previously thought unsuitably low for Vibrio growth are warranted. IMPORTANCE Vibrio parahaemolyticus is the leading cause of seafood-related gastroenteritis in the United States, with an estimated 45,000 cases per year. Most individuals who suffer from vibriosis consume raw or undercooked seafood, including oysters. While gastroenteritis vibriosis is usually self-limiting and treatable, V. parahaemolyticus infections are a stressor on the growing aquaculture industry. Much effort has been placed on modeling the growth of Vibrio cells in oysters in order to aid oyster growers in designing harvesting best practices and ultimately, to protect the consumer. However, ecological invasions of nonnative bacterial strains make modeling their growth complicated, as these strains are not accounted for in current models. The National Shellfish Sanitation Program (NSSP) considers 10°C (50°F) a temperature too low to enable Vibrio growth, where 15°C is considered a cutoff temperature for optimal Vibrio growth, with temperatures approaching 20°C supporting higher growth rates. However, invasive strains may be native to cooler waters. This research aimed to understand strain growth in live oysters by measuring growth rates when oysters containing ST36 strains, which may be endemic to the U.S. Pacific Northwest, were exposed to multiple temperatures postharvest. Our results will be used to aid future model development and harvesting best practices for the aquaculture industry.

Overexpression of the WAPO-A1 gene increases the number of spikelets per spike in bread wheat.

Two homoeologous QTLs for number of spikelets per spike (SPS) were mapped on chromosomes 7AL and 7BL using two wheat MAGIC populations. Sets of lines contrasting for the QTL on 7AL were developed which allowed for the validation and fine mapping of the 7AL QTL and for the identification of a previously described candidate gene, WHEAT ORTHOLOG OF APO1 (WAPO1). Using transgenic overexpression in both a low and a high SPS line, we provide a functional validation for the role of this gene in determining SPS also in hexaploid wheat. We show that the expression levels of this gene positively correlate with SPS in multiple MAGIC founder lines under field conditions as well as in transgenic lines grown in the greenhouse. This work highlights the potential use of WAPO1 in hexaploid wheat for further yield increases. The impact of WAPO1 and SPS on yield depends on other genetic and environmental factors, hence, will require a finely balanced expression level to avoid the development of detrimental pleiotropic phenotypes.

Investigating the Relationship between Nitrate, Total Dissolved Nitrogen, and Phosphate with Abundance of Pathogenic Vibrios and Harmful Algal Blooms in Rehoboth Bay, Delaware.

Vibrio spp. and phytoplankton are naturally abundant in marine environments. Recent studies have suggested that the co-occurrence of phytoplankton and the pathogenic bacterium Vibrio parahaemolyticus is due to shared ecological factors, such as nutrient requirements. We compared these communities at two locations in the Delaware Inland Bays, representing a site with high anthropogenic inputs (Torquay Canal) and a less developed area (Sloan Cove). In 2017 to 2018, using light microscopy, we were able to identify the presence of many bloom-forming algal species, such as Karlodinium veneficum, Dinophysis acuminata, Heterosigma akashiwo, and Chattonella subsalsa. Dinoflagellate biomass was higher at Torquay Canal than that at Sloan Cove. D. acuminata and Chloromorum toxicum were found only at Torquay Canal and were not observed in Sloan Cove. Most probable number real-time PCR revealed V. parahaemolyticus and Vibrio vulnificus in environmental samples. The abundance of vibrios and their virulence genes varied between sites, with a significant association between total dissolved nitrogen (TDN), PO4-, total dissolved phosphorus (TDP), and pathogenic markers. A generalized linear model revealed that principal component 1 of environmental factors (temperature, dissolved oxygen, salinity, TDN, PO4-, TDP, NO3:NO2, NO2-, and NH4+) was the best at detecting total (tlh+) V. parahaemolyticus, suggesting that they are the prime drivers for the growth and distribution of pathogenic Vibrio spp. IMPORTANCE Vibrio-associated illnesses have been expanding globally over the past several decades (A. Newton, M. Kendall, D. J. Vugia, O. L. Henao, and B. E. Mahon, Clin Infect Dis 54:S391-S395, 2012, https://doi.org/10.1093/cid/cis243). Many studies have linked this expansion with an increase in global temperature (J. Martinez-Urtaza, B. C. John, J. Trinanes, and A. DePaola, Food Res Int 43:10, 2010, https://doi.org/10.1016/j.foodres.2010.04.001; L. Vezzulli, R. R. Colwell, and C. Pruzzo, Microb Ecol 65:817-825, 2013, https://doi.org/10.1007/s00248-012-0163-2; R. N. Paranjpye, W. B. Nilsson, M. Liermann, and E. D. Hilborn, FEMS Microbiol Ecol 91:fiv121, 2015, https://doi.org/10.1093/femsec/fiv121). Temperature and salinity are the two major factors affecting the distribution of Vibrio spp. (D. Ceccarelli and R. R. Colwell, Front Microbiol 5:256, 2014, https://doi.org/10.3389/fmicb.2014.00256). However, Vibrio sp. abundance can also be affected by nutrient load and marine plankton blooms (V. J. McKenzie and A. R. Townsend, EcoHealth 4:384-396, 2007; L. Vezzulli, C. Pruzzo, A. Huq, and R. R. Colwell, Environ Microbiol Rep 2:27-33, 2010, https://doi.org/10.1111/j.1758-2229.2009.00128.x; S. Liu, Z. Jiang, Y. Deng, Y. Wu, J. Zhang, et al. Microbiologyopen 7:e00600, 2018, https://doi.org/10.1002/mbo3.600). The expansion of Vibrio spp. in marine environments calls for a deeper understanding of the biotic and abiotic factors that play a role in their abundance. We observed that pathogenic Vibrio spp. were most abundant in areas that favor the proliferation of harmful algal bloom (HAB) species. These results can inform managers, researchers, and oyster growers on factors that can influence the growth and distribution of pathogenic Vibrio spp. in the Delaware Inland Bays.

Toward Rapid, Widely Available Autologous CAR-T Cell Therapy - Artificial Intelligence and Automation Enabling the Smart Manufacturing Hospital.

CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.

Demystifying Esophageal Lichen Planus: A Comprehensive Review of a Rare Disease You Will See in Practice.

Lichen planus (LP) is a chronic inflammatory disorder that often affects the skin, hair, nails, and mucus membranes. Although esophageal involvement has traditionally been felt to be rare, recent reports suggest that it is often unrecognized or misdiagnosed. The diagnoses of esophageal lichen planus can be challenging and is suspected based on patients' endoscopic and histologic findings and in the context of their clinical history and physical examination. Physicians must have an index of suspicion, particularly in older white women and in those patients with an atypical esophagitis or stricturing disease, which do not respond to traditional treatment. Currently, there are limited data on esophageal lichen planus patients, and no formal management guidelines for this disease, which all gastroenterologists will see in practice. This article reviews the etiology and histopathology of LP and provides a comprehensive discussion of the clinical features, diagnosis, and management of esophageal disease from the gastroenterologist's perspective. Finally, we address the esophageal complications of LP.