A Second-Generation (44-Channel) Suprachoroidal Retinal Prosthesis: A Single-Arm Clinical Trial of Feasibility.

Purpose: To assess the feasibility of a second-generation (44-channel) suprachoroidal retinal prosthesis for provision of functional vision in recipients with end-stage retinitis pigmentosa (RP) over 2.7 years. Design: Prospective, single-arm, unmasked interventional clinical trial. Participants: Four participants, with advanced RP and bare-light perception vision. Methods: The 44-channel suprachoroidal retinal prosthesis was implanted in the worse-seeing eye. Device stability, functionality, and adverse events were investigated at approximately 12-week intervals up to 140 weeks (2.7 years) postdevice activation. Main Outcome Measures: Serious adverse event (SAE) reporting, visual response outcomes, functional vision outcomes, and quality-of-life outcomes. Results: All 4 participants (aged 39-66 years, 3 males) were successfully implanted in 2018, and there were no device-related SAEs over the duration of the study. A mild postoperative subretinal hemorrhage was detected in 2 recipients, which cleared spontaneously within 2 weeks. OCT confirmed device stability and position under the macula. Improvements in localization abilities were demonstrated for all 4 participants in screen-based, tabletop, and orientation and mobility tasks. In addition, 3 of 4 participants recorded improvements in motion discrimination and 2 of 4 participants recorded substantial improvements in spatial discrimination and identification of tabletop objects. Participants reported their unsupervised use of the device included exploring new environments, detecting people, and safely navigating around obstacles. A positive effect of the implant on participants' daily lives in their local environments was confirmed by an orientation and mobility assessor and participant self-report. Emotional well-being was not impacted by device implantation or usage. Conclusions: The completed clinical study demonstrates that the suprachoroidal prosthesis raises no safety concerns and provides improvements in functional vision, activities of daily living, and observer-rated quality of life. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning.

Purpose: To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation. Design: Retrospective multicenter study. Participants: Patients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007-2017) or Mayo Clinic Rochester (2015-2023). Methods: Multimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. Machine learning models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating characteristic curve (AUROC). The Wills Eye Hospital cohort was used for training and testing (80% training-20% testing) with fivefold cross validation. The Mayo Clinic cohort provided external validation. Model performance was characterized by AUROC and area under precision-recall curve (AUPRC). Models were interrogated using SHapley Additive exPlanations (SHAP) to identify the features most predictive of conversion from nevus to melanoma. Differences in AUROC and AUPRC between models were tested using 10 000 bootstrap samples with replacement and results. Main Outcome Measures: Area under receiver operating curve and AUPRC for each ML model. Results: There were 2870 nevi included in the study, with conversion to melanoma confirmed in 128 cases. Simple AI Nevus Transformation System (SAINTS; XGBoost) was the top-performing model in the test cohort [pooled AUROC 0.864 (95% confidence interval (CI): 0.864-0.865), pooled AUPRC 0.244 (95% CI: 0.243-0.246)] and in the external validation cohort [pooled AUROC 0.931 (95% CI: 0.930-0.931), pooled AUPRC 0.533 (95% CI: 0.531-0.535)]. Other models also had good discriminative performance: LGBM (test set pooled AUROC 0.831, validation set pooled AUROC 0.815), Random Forest (test set pooled AUROC 0.812, validation set pooled AUROC 0.866), and Extra Tree (test set pooled AUROC 0.826, validation set pooled AUROC 0.915). A model including only nevi with at least 5 years of follow-up demonstrated the best performance in AUPRC (test: pooled 0.592 (95% CI: 0.590-0.594); validation: pooled 0.656 [95% CI: 0.655-0.657]). The top 5 features in SAINTS by SHAP values were: tumor thickness, largest tumor basal diameter, tumor shape, distance to optic nerve, and subretinal fluid extent. Conclusions: We demonstrate accuracy and generalizability of a ML model for predicting choroidal nevus transformation to melanoma based on multimodal imaging. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A narrative review of gas separation and conservation technologies during xenon anesthesia.

Xenon gas has significant advantages over conventional general anesthetic agents but its use has been limited by the cost associated with its production. Xenon also has significant potential for medical use in the treatment of acquired brain injuries and for mental health disorders. As the demand for xenon gas from other industries increases, the costs associated with its medical use are only likely to increase. One solution to mitigate the significant cost of xenon use in research or medical care is the conservation of xenon gas. During delivery of xenon anesthesia, this can be achieved either by separating xenon from the other gases within the anesthetic circuit, conserving xenon and allowing other gases to be excluded from the circuit, or by selectively recapturing xenon utilized during the anesthetic episode at the conclusion of the case. Several technologies, including the pressurization and cooling of gas mixtures, the utilization of gas selective membranes and the utilization of gas selective adsorbents have been described in the literature for this purpose. These techniques are described in this narrative review along with important clinical context that informs how these technologies might be best applied. Whilst these technologies are discussed in the context of xenon general anesthesia, they could be applied in the delivery of xenon gas inhalation for other therapeutic purposes.

An absolute quantification method for selected MFGM proteins in infant formula using targeted proteomics.

Milk fat globule membrane (MFGM) proteins are receiving increased attention due to their reported benefits for human health, particularly in infant populations. Challenges exist in MFGM protein quantification due to their low quantities, complex chemistry, interactions with other matrix components, and the high matrix complexity. In this study, a subset of four MFGM proteins were selected as relevant targets for identification and quantification in an infant formula (IF) matrix: butyrophilin, mucin 1, xanthine dehydrogenase/oxidase, and perilipin 2. An analytical protocol for absolute quantification of these four MFGM proteins in IF was successfully developed and validated. Additionally, the feasibility to apply the method in selected MFGM ingredients was also demonstrated. The method demonstrated good performance; high accuracy and robustness, low LOQ and uncertainty, while controlling the matrix effect. This study represents a report of an analytical method to quantify selected MFGM proteins in IF.

Persistent symptoms after COVID-19: an Australian stratified random health survey on long COVID.

OBJECTIVE: To determine the impact of persistent symptoms after coronavirus disease 2019 (COVID-19) in an Australian population. DESIGN, SETTING, PARTICIPANTS: We conducted a statewide health survey of a stratified random sample of adults who had had a confirmed acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19-positive group) and their close contacts (control group). The sample was drawn from Victoria's COVID-19 database between January 2020 and October 2022. Data were collected from 12 688 survey respondents between September 2022 and April 2023 (11 174 in the COVID-19-positive group and 1514 in the control group). MAIN OUTCOME MEASURES: Persistent new symptoms, recovery, and daily function using validated questionnaires for fatigue, neurocognitive symptoms, anxiety, depression and quality of life. RESULTS: At a mean of 12.6 months after infection, 4560 respondents in the COVID-19-positive group (39.1%; 95% CI, 37.9-40.3%) reported at least one persistent new symptom, compared with 216 respondents in the control group (20.8%; 95% CI, 18.5-23.1%). A total of 1656 respondents (14.2%; 95% CI, 13.4-15.0%) were classified as having clinical long COVID using the criteria of at least one persistent new symptom and less than 80% recovery three months after the infection. Of the respondents with clinical long COVID, 535 (3.2%; 95% CI, 2.6-3.8%) reported at least moderate problems with usual activities at 12 months after their infection. The proportion of respondents with clinical long COVID was lower for those with more recent infections. The risk factors for clinical long COVID were female sex, age 40-49 years, infection severity, chronic illness, and past anxiety or depression. Factors associated with a decreased risk of having clinical long COVID included infection when the Omicron strain was dominant and infection when the Delta strain was dominant, as compared with when the ancestral strain of the virus was dominant. CONCLUSION: Persistent symptoms after COVID-19 are common, though with a lower incidence following infection from less virulent strains. Although long COVID can be largely managed in primary care, a minority of people who have persistent symptoms and impaired function may require specialist care pathways, the effectiveness of which should be a focus of future research.

Treatment Outcomes of Four-Implant-Retained Maxillary Palateless Overdenture: A 5-Year Observational Study.

INTRODUCTION: Palateless overdentures (PLODs) provide advantages of improved taste perception and retention over conventional dentures. We aimed to evaluate 5-year outcomes of four implant maxillary PLODs. METHODS: In this prospective observational study, edentulous participants were enrolled. A new conventional maxillary denture was prepared followed by implant placement and insertion of four implant retained maxillary PLOD. Oral health quality of life was assessed using the 49-item Oral Health Impact Profile (OHIP-49) at multiple intervals over 5 years along with biological and mechanical outcomes. RESULTS: Nine patients were evaluated at year 5. The mean age was 68 years, and six of the nine patients were males. The cumulative survival rate of implants was 100% while the implant success rate was 86%. Nylon retentive replacement was the most common encountered complication noted approximately four times per patient over 5 years. From a mean OHIP-49 severity score of 71.2 at baseline, severity scores decreased to 23.9 (p < 0.001) at year 1, denoting a clinically meaningful and statistically significant improvement in oral health quality of life. The lowered OHIP-49 severity scores remained stable throughout 5 years of follow-up. CONCLUSION: Four implant supported maxillary overdenture appears to have good patient perceived, biological and mechanical outcomes over 5 years. Fully powered studies are needed to replicate these findings.

Refined matrix completion for spectrum estimation of heart rate variability.

Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.

Programmable Piperazine Synthesis via Organic Photoredox Catalysis.

Piperazine cores have long been identified as privileged scaffolds in the development of pharmaceutical compounds. Despite this, the facile synthesis of diverse C-substituted piperazines remains a challenge without prefunctionalized substrates/cores. Herein, we describe a programmable approach to highly diversifiable piperazine cores, which circumvents the typical need for radical precursors. The use of organic photoredox catalysis renders this method operationally simple, as direct substrate oxidation followed by 6-endo-trig radical cyclization with in situ generated imines may furnish the product. Additionally, the photoredox-catalyzed anti-Markovnikov hydroamination of readily accessible ene-carbamates provides a modular approach to functionalized diamine starting materials which are shown to generate more complex piperazine cores. A wide range of both carbonyl and amine condensation partners were shown to be compatible with this system in good to excellent yield.

A generalised computer vision model for improved glaucoma screening using fundus images.

IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images could enhance early and accurate disease detection. OBJECTIVE: To develop and validate a generalised deep-learning-based algorithm for screening glaucoma using fundus image. DESIGN, SETTING AND PARTICIPANTS: The glaucomatous fundus data were collected from 20 publicly accessible databases worldwide, resulting in 18,468 images from multiple clinical settings, of which 10,900 were classified as healthy and 7568 as glaucoma. All the data were evaluated and downsized to fit the model's input requirements. The potential model was selected from 20 pre-trained models and trained on the whole dataset except Drishti-GS. The best-performing model was further trained to classify healthy and glaucomatous fundus images using Fastai and PyTorch libraries. MAIN OUTCOMES AND MEASURES: The model's performance was compared against the actual class using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, precision and the F1-score. RESULTS: The high discriminative ability of the best-performing model was evaluated on a dataset comprising 1364 glaucomatous discs and 2047 healthy discs. The model reflected robust performance metrics, with an AUROC of 0.9920 (95% CI: 0.9920-0.9921) for both the glaucoma and healthy classes. The sensitivity, specificity, accuracy, precision, recall and F1-scores were consistently higher than 0.9530 for both classes. The model performed well on an external validation set of the Drishti-GS dataset, with an AUROC of 0.8751 and an accuracy of 0.8713. CONCLUSIONS AND RELEVANCE: This study demonstrated the high efficacy of our classification model in distinguishing between glaucomatous and healthy discs. However, the model's accuracy slightly dropped when evaluated on unseen data, indicating potential inconsistencies among the datasets-the model needs to be refined and validated on larger, more diverse datasets to ensure reliability and generalisability. Despite this, our model can be utilised for screening glaucoma at the population level.

Nepali oral microbiomes reflect a gradient of lifestyles from traditional to industrialized.

BACKGROUND: Lifestyle plays an important role in shaping the gut microbiome. However, its contributions to the oral microbiome remain less clear, due to the confounding effects of geography and methodology in investigations of populations studied to date. Furthermore, while the oral microbiome seems to differ between foraging and industrialized populations, we lack insight into whether transitions to and away from agrarian lifestyles shape the oral microbiota. Given the growing interest in so-called "vanishing microbiomes" potentially being a risk factor for increased disease prevalence in industrialized populations, it is important that we distinguish lifestyle from geography in the study of microbiomes across populations. RESULTS: Here, we investigate salivary microbiomes of 63 Nepali individuals representing a spectrum of lifestyles: foraging, subsistence farming (individuals that transitioned from foraging to farming within the last 50 years), agriculturalists (individuals that have transitioned to farming for at least 300 years), and industrialists (expatriates that immigrated to the USA within the last 20 years). We characterize the role of lifestyle in microbial diversity, identify microbes that differ between lifestyles, and pinpoint specific lifestyle factors that may be contributing to differences in the microbiomes across populations. Contrary to prevailing views, when geography is controlled for, oral microbiome alpha diversity does not differ significantly across lifestyles. Microbiome composition, however, follows the gradient of lifestyles from foraging through agrarianism to industrialism, supporting the notion that lifestyle indeed plays a role in the oral microbiome. Relative abundances of several individual taxa, including Streptobacillus and an unclassified Porphyromonadaceae genus, also mirror lifestyle. Finally, we identify specific lifestyle factors associated with microbiome composition across the gradient of lifestyles, including smoking and grain sources. CONCLUSION: Our findings demonstrate that by studying populations within Nepal, we can isolate an important role of lifestyle in determining oral microbiome composition. In doing so, we highlight the potential contributions of several lifestyle factors, underlining the importance of carefully examining the oral microbiome across lifestyles to improve our understanding of global microbiomes. Video Abstract.

Estimating the Effect of Coronavirus Disease 2019 (COVID-19) Vaccination and Infection Variant on Post-COVID-19 Venous Thrombosis or Embolism Risk.

Background: Previous research has shown that vaccination reduces risk of post-coronavirus disease 2019 (COVID-19) venous thrombosis or embolism (VTE), but the effect of vaccine boosting on post-COVID-19 VTE risk reduction is unclear. We sought to estimate the effect of COVID-19 vaccination on the risk of post-COVID-19 VTE and to examine if the magnitude of this association differed among variant eras. Methods: We performed a case-control study of Military Health System (MHS) beneficiaries who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020-2022. Cases were defined as those with medically attended VTE within 90 days after their first positive SARS-CoV-2 test; controls were defined as SARS-CoV-2 infections without incident VTE by 90 days. Multivariate logistic regression estimated the odds of post-SARS-CoV-2 VTE based on pre-COVID-19 vaccine status, adjusting for other VTE risk factors. Results: A total of 4646 MHS beneficiaries were included in this analysis; 1370 received a primary vaccine series and a further 790 received at least 1 booster at time of infection; 71 had VTE within 90 days of SARS-CoV-2 infection. Those who were vaccinated had lower odds of VTE (adjusted odds ratio [95% confidence interval]) compared to the unvaccinated following infection (primary series: 0.28 [.13-.62]; booster dose: 0.06 [.01-.46]). Post-COVID-19 VTE risk was lowest during the Omicron era, but VTEs were too rare to examine for an interaction of variant era and vaccine effect. Conclusions: Among MHS beneficiaries, COVID-19 vaccination was associated with a reduced risk of post-COVID-19 VTE diagnosis; estimated risk reduction was larger among those who received a booster.

Perceptions of key informant neurologists before implementing anti-amyloid drugs in the Spanish departments of neurology.

BACKGROUND: A deep knowledge of the healthcare system and the organization of neurology departments is important for planning and optimizing changes to facilitate the successful implementation of anti-amyloid antibodies treatments. OBJECTIVE: We aimed to assess the necessary changes prior to introducing these therapies in our setting. METHODS: We conducted a key informant survey among heads of departments of neurology from 16 hospitals in Spain. The questionnaire comprised questions about changes in the organization and functioning of the departments of neurology with the introduction of anti-amyloid drugs, changes in diagnosis and patient care, use of diagnostic techniques, patients, families and public information, resources allocation, and research. RESULTS: Sixteen key informants completed the survey. They strongly agreed that the introduction of anti-amyloid drugs will impact the functioning of neurology services, especially in hospitals with dementia units. Consensus was reached regarding referring all Alzheimer's disease patients eligible for therapy to dementia units. There was also agreement on the need to expand the neurology services, day hospital units, extend visit durations, and hire more professionals, especially neurologists, neuropsychologists, and nuclear medicine physicians. Furthermore, consensus was achieved on increasing the use of MRI, amyloid PET, cerebrospinal fluid biomarkers, APOE genotyping, and the necessity of advancing blood biomarkers and tau tracers. CONCLUSIONS: Our study highlights the need for extensive changes within Spanish neurological departments to effectively integrate anti-amyloid antibodies. Implementing these changes is essential for the timely and equitable adoption of novel therapies.

Cohesive Living Bacterial Films with Tunable Mechanical Properties from Cell Surface Protein Display.

Engineered living materials (ELMs) constitute a novel class of functional materials that contain living organisms. The mechanical properties of many such systems are dominated by the polymeric matrices used to encapsulate the cellular components of the material, making it hard to tune the mechanical behavior through genetic manipulation. To address this issue, we have developed living materials in which mechanical properties are controlled by the cell-surface display of engineered proteins. Here, we show that engineered Esherichia coli cells outfitted with surface-displayed elastin-like proteins (ELPs, designated E6) grow into soft, cohesive bacterial films with biaxial moduli around 14 kPa. When subjected to bulge-testing, such films yielded at strains of approximately 10%. Introduction of a single cysteine residue near the exposed N-terminus of the ELP (to afford a protein designated CE6) increases the film modulus 3-fold to 44 kPa and eliminates the yielding behavior. When subjected to oscillatory stress, films prepared from E. coli strains bearing CE6 exhibit modest hysteresis and full strain recovery; in E6 films much more significant hysteresis and substantial plastic deformation are observed. CE6 films heal autonomously after damage, with the biaxial modulus fully restored after a few hours. This work establishes an approach to living materials with genetically programmable mechanical properties and a capacity for self-healing. Such materials may find application in biomanufacturing, biosensing, and bioremediation.

Worsening sleep predicts next-week suicidal ideation in a high-risk adolescent outpatient treatment sample.

INTRODUCTION: Suicidal thoughts and behaviors often onset during adolescence, constituting a major public health concern. Despite rising rates in youth, psychotherapeutic and pharmacological treatment outcomes remain meager, indicating a need to identify modifiable risk factors. Experts suggest sleep may serve as a promising clinical indicator of risk and treatment progress, yet few studies have examined acute temporal associations between sleep and suicidal ideation. METHODS: Participants were depressed and suicidal adolescents (age 13-19, n = 311) attending an intensive outpatient program (IOP). Patients completed weekly self-report assessments of sleep, depression, and suicidal ideation throughout IOP, with an average of 5 assessments over 30 days. RESULTS: Greater overall sleep difficulties, as well as within-person increases in sleep difficulties, were predictive of greater depression severity and suicidal ideation at subsequent assessments, above and beyond previous levels of symptoms. The reverse associations were not found. Inclusion of within-person changes in sleep difficulties significantly improved model fit compared to inclusion of overall sleep difficulties alone. DISCUSSION: This study provides additional evidence for a prospective relationship between worsening sleep difficulties and suicidal ideation in an IOP youth sample. Worsening sleep may be an important clinical indicator of subsequent depression and suicide risk among adolescents in treatment.

The Validity of Impressions as a Media Dose Metric in a Tobacco Public Education Campaign Evaluation: Observational Study.

BACKGROUND: Evaluation research increasingly needs alternatives to target or gross rating points to comprehensively measure total exposure to modern multichannel public education campaigns that use multiple channels, including TV, radio, digital video, and paid social media, among others. Ratings data typically only capture delivery of broadcast media (TV and radio) and excludes other channels. Studies are needed to validate objective cross-channel metrics such as impressions against self-reported exposure to campaign messages. OBJECTIVE: This study aimed to examine whether higher a volume of total media campaign impressions is predictive of individual-level self-reported campaign exposure in California. METHODS: We analyzed over 3 years of advertisement impressions from the California Tobacco Prevention Program's statewide tobacco education campaigns from August 2019 through December 2022. Impressions data varied across designated market areas (DMAs) and across time. These data were merged to individual respondents from 45 waves of panel survey data of Californians aged 18-55 years (N=151,649). Impressions were merged to respondents based on respondents' DMAs and time of survey completion. We used logistic regression to estimate the odds of respondents' campaign recall as a function of cumulative and past 3-month impressions delivered to each respondent's DMA. RESULTS: Cumulative impressions were positively and significantly associated with recall of each of the Flavors Hook Kids (odds ratio [OR] 1.15, P<.001), Dark Balloons and Apartment (OR 1.20, P<.001), We Are Not Profit (OR 1.36, P<.001), Tell Your Story (E-cigarette, or Vaping, product use Associated Lung Injury; OR 1.06, P<.05), and Thrown Away and Little Big Lies (OR 1.05, P<.01) campaigns. Impressions delivered in the past 3 months were associated with recall of the Flavors Hook Kids (OR 1.13, P<.001), Dark Balloons and Apartment (OR 1.08, P<.001), We Are Not Profit (OR 1.14, P<.001), and Thrown Away and Little Big Lies (OR 1.04, P<.001) campaigns. Past 3-month impressions were not significantly associated with Tell Your Story campaign recall. Overall, magnitudes of these associations were greater for cumulative impressions. We visualize recall based on postestimation predicted values from our multivariate logistic regression models. CONCLUSIONS: Variation in cumulative impressions for California Tobacco Prevention Program's long-term multichannel tobacco education campaign is predictive of increased self-reported campaign recall, suggesting that impressions may be a valid proxy for potential campaign exposure. The use of impressions for purposes of evaluating public education campaigns may help address current methodological limitations arising from the fragmented nature of modern multichannel media campaigns.

Widespread pain phenotypes impact treatment efficacy results in randomized clinical trials for interstitial cystitis/bladder pain syndrome: a Multidisciplinary Approach to the Study of Chronic Pelvic Pain network study.

ABSTRACT: Pain clinical trials are notoriously complex and often inefficient in demonstrating efficacy, even for known efficacious treatments. A major issue is the difficulty in the a priori identification of specific phenotypes to include in the study population. Recent work has identified the extent of widespread pain as an important determinant of the likelihood of response to therapy, but it has not been tested in clinical trials for the treatment of interstitial cystitis/bladder pain syndrome (IC/BPS). We explored this hypothesis using data from 3 previously published trials testing treatments for IC/BPS, which suggested modest benefits but did not meet a priori primary outcome statistical significance criteria. Importantly, these studies also collected symptom questionnaire data that allowed us to retrospectively identify participants with and without widespread pain. Analyzing the treatment by the degree of widespread pain revealed a difference in outcome and statistical significance level for each trial. Participants with predominately local pain (ie, limited widespread pain symptoms) responded to therapy targeting local symptoms, whereas those with widespread pain did not. Alternatively, participants with widespread pain beyond their local pelvic pain responded to more centrally acting treatments. Our results suggest that differentiating patients based on widespread vs more localized pain is a key consideration for designing future clinical trials for conditions with variable pain profiles, such as IC/BPS and potentially other pain-based syndromic disorders.

Myxedema Coma-associated Pancytopenia: A Case Report.

Pancytopenia is defined as a reduction in red blood cells, white blood cells, and platelets, and can pose as a diagnostic challenge due to the multitude of causes. Myxedema coma is the manifestation of severe untreated hypothyroidism. This case report presents a rare instance of myxedema coma-associated pancytopenia in a 53-year-old man with a history of untreated hypothyroidism. The patient presented with altered mental status and vital instability, and on further workup was found to have pancytopenia. During his hospital stay his symptomatic hypothyroidism was identified, and he was treated with intravenous levothyroxine, hydrocortisone, and supportive care. The patient's clinical status improved gradually, with normalized blood counts upon discharge. This case underscores the significance of considering myxedema coma in the differential diagnosis of pancytopenia, especially in older patients with limited healthcare access. Increased awareness of this association can aid clinicians in timely diagnosis and management, preventing potential complications associated with untreated hypothyroidism.

Integration across biophysical scales identifies molecular and cellular correlates of person-to-person variability in human brain connectivity.

Brain connectivity arises from interactions across biophysical scales, ranging from molecular to cellular to anatomical to network level. To date, there has been little progress toward integrated analysis across these scales. To bridge this gap, from a unique cohort of 98 individuals, we collected antemortem neuroimaging and genetic data, as well as postmortem dendritic spine morphometric, proteomic and gene expression data from the superior frontal and inferior temporal gyri. Through the integration of the molecular and dendritic spine morphology data, we identified hundreds of proteins that explain interindividual differences in functional connectivity and structural covariation. These proteins are enriched for synaptic structures and functions, energy metabolism and RNA processing. By integrating data at the genetic, molecular, subcellular and tissue levels, we link specific biochemical changes at synapses to connectivity between brain regions. These results demonstrate the feasibility of integrating data from vastly different biophysical scales to provide a more comprehensive understanding of brain connectivity.