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Abstract Synucleinopathies, including Parkinson's disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB), are neurodegenerative disorders caused by the accumulation of misfolded alpha‐synuclein protein. Developing effective vaccines against synucleinopathies is challenging due to the difficulty of stimulating an immune‐specific response against alpha‐synuclein without causing harmful autoimmune reactions, selectively targeting only pathological forms of alpha‐synuclein. Previous attempts using linear peptides and epitopes without control of the antigen structure failed in clinical trials. The immune system was unable to distinguish between native alpha‐synuclein and its amyloid form. The prion domain of the fungal HET‐s protein was selected as a scaffold to introduce select epitopes from the surface of alpha‐synuclein fibrils. Four vaccine candidates were generated by introducing specific amino acid substitutions onto the surface of the scaffold protein. The approach successfully mimicked the stacking of the parallel in‐register beta‐sheet structure seen in alpha‐synuclein fibrils. All vaccine candidates induced substantial levels of IgG antibodies that recognized pathological alpha‐synuclein fibrils derived from a synucleinopathy mouse model. Furthermore, the antisera recognized pathological alpha‐synuclein aggregates in brain lysates from patients who died from DLB, MSA, or PD, but did not recognize linear alpha‐synuclein peptides. Our approach, based on the rational design of vaccines using the structure of alpha‐synuclein amyloid fibrils and strict control over the exposed antigen structure used for immunization, as well as the ability to mimic aggregated alpha‐synuclein, provides a promising avenue toward developing effective vaccines against alpha‐synuclein fibrils.

Uterine leiomyomas are common, benign neoplasms of the uterine smooth muscle. Leiomyomatosis is uncommon and causes development of multiple leiomyomas that can manifest as intravascular leiomyomatosis (IVL). We present the case of a 46-year-old female with IVL extending from the right gonadal vein to the right atrium and pulmonary arteries with an independent renal cell carcinoma of the right kidney. She underwent successful open right radical nephrectomy, inferior vena caval tumor thrombectomy and pulmonary embolectomy. While there was initial concern for hereditary renal cell carcinoma, final histologic testing did not support the diagnosis.

Abstract Background Knowledge is growing on how gut microbiota are established, but the effects of maternal symbiotic microbes throughout early microbial successions in birds remain elusive. In this study, we examined the contributions and transmission modes of maternal microbes into the neonatal microbiota of a passerine, the zebra finch (Taeniopygia guttata), based on fostering experiments. Results Using 16S rRNA amplicon sequencing, we found that zebra finch chicks raised by their biological or foster parents (the society finch Lonchura striata domestica) had gut microbial communities converging with those of the parents that reared them. Moreover, source-tracking models revealed high contribution of zebra finches’ oral cavity/crop microbiota to their chicks’ early gut microbiota, which were largely replaced by the parental gut microbiota at later stages. The results suggest that oral feeding only affects the early stage of hatchling gut microbial development. Conclusions Our study indicates that passerine chicks mainly acquire symbionts through indirect maternal transmission—passive environmental uptake from nests that were smeared with the intestinal and cloacal microbes of parents that raised them. Gut microbial diversity was low in hand-reared chicks, emphasizing the importance of parental care in shaping the gut microbiota. In addition, several probiotics were found in chicks fostered by society finches, which are excellent foster parents for other finches in bird farms and hosts of brood parasitism by zebra finches in aviaries; this finding implies that avian species that can transfer probiotics to chicks may become selectively preferred hosts of brood parasitism in nature. Video Abstract

Bovine spongiform encephalopathy (BSE) is a prion disease of cattle that is caused by the misfolding of the cellular prion protein (PrPC) into an infectious conformation (PrPSc). PrPC is a predominantly α-helical membrane protein that misfolds into a β-sheet rich, infectious state, which has a high propensity to self-assemble into amyloid fibrils. Three strains of BSE prions can cause prion disease in cattle, including classical BSE (C-type) and two atypical strains, named L-type and H-type BSE. To date, there is no detailed information available about the structure of any of the infectious BSE prion strains. In this study, we purified L-type BSE prions from transgenic mouse brains and investigated their biochemical and ultrastructural characteristics using electron microscopy, image processing, and immunogold labeling techniques. By using phosphotungstate anions (PTA) to precipitate PrPSc combined with sucrose gradient centrifugation, a high yield of proteinase K-resistant BSE amyloid fibrils was obtained. A morphological examination using electron microscopy, two-dimensional class averages, and three-dimensional reconstructions revealed two structural classes of L-type BSE amyloid fibrils; fibrils that consisted of two protofilaments with a central gap and an average width of 22.5 nm and one-protofilament fibrils that were 10.6 nm wide. The one-protofilament fibrils were found to be more abundant compared to the thicker two-protofilament fibrils. Both fibrillar assemblies were successfully decorated with monoclonal antibodies against N- and C-terminal epitopes of PrP using immunogold-labeling techniques, confirming the presence of polypeptides that span residues 100-110 to 227-237. The fact that the one-protofilament fibrils contain both N- and C-terminal PrP epitopes constrains molecular models for the structure of the infectious conformer in favour of a compact four-rung β-solenoid fold.

The amyloid β peptide (Aβ) is a key player in the etiology of Alzheimer disease (AD), yet a systematic investigation of its molecular interactions has not been reported. Here we identified by quantitative mass spectrometry proteins in human brain extract that bind to oligomeric Aβ1-42 (oAβ1-42) and/or monomeric Aβ1-42 (mAβ1-42) baits. Remarkably, the cyclic neuroendocrine peptide somatostatin-14 (SST14) was observed to be the most selectively enriched oAβ1-42 binder. The binding interface comprises a central tryptophan within SST14 and the N-terminus of Aβ1-42. The presence of SST14 inhibited Aβ aggregation and masked the ability of several antibodies to detect Aβ. Notably, Aβ1-42, but not Aβ1-40, formed in the presence of SST14 oligomeric assemblies of 50 to 60 kDa that were visualized by gel electrophoresis, nanoparticle tracking analysis and electron microscopy. These findings may be relevant for Aβ-directed diagnostics and may signify a role of SST14 in the etiology of AD.

This paper develops a generalizable systems framework to analyze the food-energy-water (FEW) nexus from an urban systems perspective, connecting in- and trans-boundary interactions, quantifying multiple environmental impacts of community-wide FEW provisioning to cities, and visualizing FEW supply-chain risks posed to cities by the environment. Delhi’s community-wide food demand includes household consumption by socio-economic-strata, visitors- and industrial food-use. This demand depends 90%, 76%, and 86% on trans-boundary supply of FEW, respectively. Supply chain data reveal unique features of trans-boundary FEW production regions (e.g. irrigation-electricity needs and GHG intensities of power-plants), yielding supply chain-informed coupled energy-water-GHG footprints of FEW provisioning to Delhi. Agri-food supply contributes to both GHG (19%) and water-footprints (72%–82%) of Delhi’s FEW provisioning, with milk, rice and wheat dominating these footprints. Analysis of FEW interactions within Delhi found >75% in-boundary water-use for food is for urban agriculture and >76% in-boundary energy-use for food is from cooking fuels. Food waste-to-energy and energy-intensity of commercial and industrial food preparation are key data gaps. Visualizing supply chains shows >75% of water embodied in Delhi’s FEW supply is extracted from locations over-drafting ground water. These baseline data enable evaluation of future urban FEW scenarios, comparing impacts of demand shifts, production shifts, and emerging technologies and policies, within and outside of cities.

Utilizing low-grade waste heat from industries to heat and cool homes and businesses through fourth generation district energy systems (DES) is a novel strategy to reduce energy use. This paper develops a generalizable methodology to estimate the energy saving potential for heating/cooling in 20 cities in two Chinese provinces, representing cold winter and hot summer regions respectively. We also conduct a life-cycle analysis of the new infrastructure required for energy exchange in DES. Results show that heating and cooling energy use reduction from this waste heat exchange strategy varies widely based on the mix of industrial, residential and commercial activities, and climate conditions in cities. Low-grade heat is found to be the dominant component of waste heat released by industries, which can be reused for both district heating and cooling in fourth generation DES, yielding energy use reductions from 12%–91% (average of 58%) for heating and 24%–100% (average of 73%) for cooling energy use in the different cities based on annual exchange potential. Incorporating seasonality and multiple energy exchange pathways resulted in energy savings reductions from 0%–87%. The life-cycle impact of added infrastructure was small (

Calcium carbonate skeletons of scleractinian corals amplify light availability to their algal symbionts by diffuse scattering, optimizing photosynthetic energy acquisition. However, the mechanism of scattering and its role in coral evolution and dissolution of algal symbioses during "bleaching" events are largely unknown. Here we show that differences in skeletal fractal architecture at nano/micro-lengthscales within 96 coral taxa result in an 8-fold variation in light-scattering and considerably alter the algal light environment. We identified a continuum of properties that fall between two extremes: (1) corals with low skeletal fractality that are efficient at transporting and redistributing light throughout the colony with low scatter but are at higher risk of bleaching and (2) corals with high skeletal fractality that are inefficient at transporting and redistributing light with high scatter and are at lower risk of bleaching. While levels of excess light derived from the coral skeleton is similar in both groups, the low-scatter corals have a higher rate of light-amplification increase when symbiont concentration is reduced during bleaching, thus creating a positive feedback-loop between symbiont concentration and light-amplification that exposes the remaining symbionts to increasingly higher light intensities. By placing our findings in an evolutionary framework, in conjunction with a novel empirical index of coral bleaching susceptibility, we find significant correlations between bleaching susceptibility and light-scattering despite rich homoplasy in both characters; suggesting that the cost of enhancing light-amplification to the algae is revealed in decreased resilience of the partnership to stress.

The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. However, existing medication recommendation systems have yet to make use of all these information sources in a seamless manner, and they do not provide a justification on why a particular medication is recommended. In this work, we design a two-stage personalized medication recommender system called PREMIER that incorporates information from the EHR. We utilize the various weights in the system to compute the contributions from the information sources for the recommended medications. Our system models the drug interaction from an external drug database and the drug co-occurrence from the EHR as graphs. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Case studies demonstrate that the justifications provided by PREMIER are appropriate and aligned to clinical practices. [ABSTRACT FROM AUTHOR]

STUDY DESIGN.: A retrospective case control analysis of 48 cases of postoperative infection following spinal procedures. OBJECTIVES.: Spinal procedures that became infected after surgery were analyzed to identify the significance of preoperative and intraoperative risk factors. Characterization of the nature and timing of the infections was also performed. SUMMARY OF BACKGROUND DATA.: The rate of postoperative infection following spinal surgery varies widely depending on the nature of the procedure and the patients diagnosis. Preoperative comorbidities and risk factors also influence the likelihood of infection. METHODS.: A review of 1629 procedures performed on 1095 patients revealed that a postoperative infection developed in 48 patients (4.4%). Data regarding preoperative and intraoperative risk factors were gathered from patient charts for these and a randomly selected control group of 95 uninfected patients. For analysis, these patient groups were further divided into adult and pediatric subgroups, with an age cutoff of 18 years. Preoperative risk factors reviewed included smoking, diabetes, previous surgery, previous infection, steroid use, body mass index, and alcohol abuse. Intraoperative factors reviewed included staging of procedures, estimated blood loss, operating time, and use of allograft or instrumentation. RESULTS.: The majority of infections occurred during the early postoperative period (less than 3 months). Age >60 years, smoking, diabetes, previous surgical infection, increased body mass index, and alcohol abuse were statistically significant preoperative risk factors. The most likely procedure to be complicated by an infection was a combined anterior/posterior spinal fusion performed in a staged manner under separate anesthesia. Infections were primarily monomicrobial, although 5 patients had more than 4 organisms identified. The most common organism cultured from the wounds was Staphylococcus aureus. All patients were treated with surgical irrigation and dbridement, and appropriate antibiotics to treat the cultured organism. CONCLUSIONS.: Aggressive treatment of patients undergoing complex or prolonged spinal procedures is essential to prevent and treat infections. Understanding a patients preoperative risk factors may help the physician to optimize a patients preoperative condition. Additionally, awareness of critical intraoperative parameters will helpto optimize surgical treatment. It may be appropriate to increase the duration of prophylactic antibiotics or implement other measures to decrease the incidence of infection for high risk patients. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical trials have demonstrated that initiating oral anti-diabetic drugs (OADs) significantly reduce glycated hemoglobin (HbA1c) levels. However, variability in lifestyle modifications and OAD adherence impact on their actual effect on glycemic control. Furthermore, evidence on dose adjustments and discontinuation of OAD on HbA1c is lacking. This study aims to use real-world data to determine the effect of OAD initiation, up-titration, down-titration, and discontinuation on HbA1c levels, among Asian patients managed in primary care. Methods A retrospective cohort study over a 5-year period, from Jan 2015 to Dec 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of type 2 diabetes mellitus (T2DM) managed by a network of primary care clinics in Singapore. Nine OADs from five different classes (biguanides, sulphonyurea, dipeptidyl peptidase-4 [DPP-4] inhibitors, sodium-glucose cotransporter-2 [SGLT-2] inhibitors, and alpha-glucosidase inhibitors) were evaluated. Patients were grouped into “No OAD”, “Non-titrators,” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the various OAD titrations were identified. Subsequently, a descriptive analysis of HbA1c values before and after each titration was performed to compute a mean difference for each unique titration identified. Results Among the cohort of 57,910 patients, 43,338 of them had at least one OAD titration, with a total of 76,990 pairs of HbA1c values associated with an OAD titration. There were a total of 206 unique OAD titrations. Overall, initiation of OADs resulted in a reduction of HbA1c by 3 to 12 mmol/mol (0.3 to 1.1%), respectively. These results were slightly lower than those reported in clinical trials of 6 to 14 mmol/mol (0.5 to 1.25%). The change of HbA1c levels due to up-titration, down-titration, and discontinuation were −1 to −8 mmol/mol (−0.1 to −0.7%), +1 to 7 mmol/mol (+0.1 to +0.6%), and +2 to 11 mmol/mol (+0.2 to +1.0%), respectively. The HbA1c lowering effect of initiating newer OADs, namely DPP-4 inhibitors and SGLT-2 inhibitors was 8 to 11 mmol/mol (0.7 to 0.9%) and 7 to 11 mmol/mol (0.6 to 1.0%), respectively. Conclusion The real-world data on Asians with T2DM in this study show that the magnitudes of OAD initiation and dose titration are marginally lower than the results from clinical trials. During shared decision-making in selecting treatment options, the results enable physicians to communicate realistic expectation of the effect of oral medications on the glycemic control of their patients in primary care.

Abstract Background Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.

Abstract Background Clinical trials have demonstrated that either initiating or up-titrating a statin dose substantially reduce Low-Density Lipoprotein-Cholesterol (LDL-C) levels. However, statin adherence in actual practice tends to be suboptimal, leading to diminished effectiveness. This study aims to use real-world data to determine the effect on LDL-C levels and LDL-C goal attainment rates, when selected statins are titrated in Asian patients. Methods A retrospective cohort study over a 5-year period, from April 2014 to March 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of Dyslipidaemia in a primary care clinic in Singapore. The statins were classified into low-intensity (LI), moderate-intensity (MI) and high-intensity (HI) groups according to the 2018 American College of Cardiology and American Heart Association (ACC/AHA) Blood Cholesterol Guidelines. Patients were grouped into “No statin”, “Non-titrators” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the mean percentage change in LDL-C and absolute change in LDL-C goal attainment rates were computed for each permutation of statin intensity titration. Results Among the cohort of 11,499 patients, with a total of 266,762 visits, there were 1962 pairs of LDL-C values associated with a statin titration. Initiation of LI, MI and HI statin resulted in a lowering of LDL-C by 21.6% (95%CI = 18.9–24.3%), 28.9% (95%CI = 25.0–32.7%) and 25.2% (95%CI = 12.8–37.7%) respectively. These were comparatively lower than results from clinical trials (30 to 63%). The change of LDL-C levels due to up-titration, down-titration, and discontinuation were − 12.4% to − 28.9%, + 13.2% to + 24.6%, and + 18.1% to + 32.1% respectively. The improvement in LDL-C goal attainment ranged from 26.5% to 47.1% when statin intensity was up-titrated. Conclusion In this study based on real-world data of Asian patients in primary care, it was shown that although statin titration substantially affected LDL-C levels and LDL-C goal attainment rates, the magnitude was lower than results reported from clinical trials. These results should be taken into consideration and provide further insight to clinicians when making statin adjustment recommendations in order to achieve LDL-C targets in clinical practice, particularly for Asian populations.

Background: The healthcare industry is the new frontier for blockchain technology. Given its properties of immutability and decentralization, blockchain represents an opportunity for unprecedented level of privacy and security for all stakeholders by ensuring data integrity while giving patients control over their own health data. On a backdrop of rising interest in blockchain in general and blockchain healthcare applications in particular, there has been a proliferation of blockchain healthcare projects over the past few years. The aim of this review is to identify and understand real-world blockchain healthcare projects that have attained commercial success in the highly competitive blockchain market. Methods and findings: A scoping review was performed in January 2021 on all projects in the CoinMarketCap database. Following a pre-defined inclusion and exclusion criteria, eligible projects were selected. A single reviewer then reviewed each project’s official website and whitepaper (where available) and performed data abstraction; 10 blockchain healthcare projects fulfilled the selection criteria. The review found that these projects made up 0.24% of the total number of actively tracked projects on CoinMarketCap. In terms of market capitalization, the total market capitalization for the projects was US$65,078,849, comprising less than 0.01% of the total market capitalization of all projects. Among the projects, the most frequent type was for personal health tracking. Conclusions: This review revealed that blockchain health projects currently comprise a small fraction of the overall number of commercially successful blockchain projects. However, because this sub-industry is still in its early stages, there are reasons to be optimistic that many more blockchain health projects will emerge and attain commercial success in future. Findings from this review done from an entrepreneurial perspective should help with the identification of future projects most likely to succeed.