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After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR after RP, and compare them with traditional regression models and nomograms.Utilizing a prospective Uro-oncology registry, 18 clinicopathological parameters of 1130 consecutive patients who underwent RP (2009-2018) were recorded, yielding over 20,000 data points for analysis. The data set was split into a 70:30 ratio for training and validation. Three ML models: Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) were studied, and compared with traditional regression models and nomograms (Kattan, CAPSURE, John Hopkins [JHH]) to predict BCR at 1, 3, and 5 years.Over a median follow-up of 70.0 months, 176 (15.6%) developed BCR, at a median time of 16.0 months (interquartile range [IQR]: 11.0-26.0). Multivariate analyses demonstrated strongest association of BCR with prostate-specific antigen (PSA) (p: 0.015), positive surgical margins (p 0.001), extraprostatic extension (p: 0.002), seminal vesicle invasion (p: 0.004), and grade group (p 0.001). The 3 ML models demonstrated good prediction of BCR at 1, 3, and 5 years, with the area under curves (AUC) of NB at 0.894, 0.876, and 0.894, RF at 0.846, 0.875, and 0.888, and SVM at 0.835, 0.850, and 0.855, respectively. All models demonstrated (1) robust accuracy (0.82), (2) good calibration with minimal overfitting, (3) longitudinal consistency across the three time points, and (4) inter-model validity. The ML models were comparable to traditional regression analyses (AUC: 0.797, 0.848, and 0.862) and outperformed the three nomograms: Kattan (AUC: 0.815, 0.798, and 0.799), JHH (AUC: 0.820, 0.757, and 0.750) and CAPSURE nomograms (AUC: 0.706, 0.720, and 0.749) (p 0.001).Supervised ML algorithms can deliver accurate performances and outperform nomograms in predicting BCR after RP. This may facilitate tailored care provisions by identifying high-risk patients who will benefit from multimodal therapy.

Expandable endoprostheses can be used to equalize limb length for pediatric patients requiring reconstruction following large bony oncologic resections. Outcomes of the Compress® Compliant Pre-Stress (CPS) spindle paired with an Orthopedic Salvage System expandable distal femur endoprosthesis have not been reported.We conducted a multi-institutional retrospective study of pediatric patients with distal femoral bone sarcomas reconstructed with the above endoprostheses. Statistical analysis utilized Kaplan-Meier survival technique and competing risk analysis.Thirty-six patients were included from five institutions. Spindle survivorship was 86.3% (95% confidence interval [CI], 67.7-93.5) at 10 years. Two patients had a failure of osseointegration (5.7%), both within 12 months. Twenty-two (59%) patients had 70 lengthening procedures, with mean expansions of 3.2 cm (range: 1-9) over 3.4 surgeries. The expandable mechanism failed in eight patients with a cumulative incidence of 16.1% (95% CI, 5.6-31.5) at 5 years. Twenty-nine patients sustained International Society of Limb Salvage failures requiring 63 unplanned surgeries. Periprosthetic joint infection occurred in six patients (16.7%). Limb preservation rate was 91% at 10 years.There is a high rate of osseointegration of the Compress® spindle among pediatric patients when coupled with an expandable implant. However, there is a high rate of expansion mechanism failure and prosthetic joint infections requiring revision surgery.Level IV, therapeutic study.

India is home to 1.3 billion people who are exposed to some of the highest levels of ambient air pollution in the world. In addition, India is one of the fastest-growing carbon-emitting countries. Here, we assess how two strategies to reuse waste-heat from coal-fired power plants and other large sources would impact PM

Pancreatic diseases have long been associated with impaired glucose control. This study sought to identify the incidence of new insulin-dependent diabetes mellitus (IDDM) after pancreatectomy and the predictive accuracy of hemoglobin A1c (HbA1c) or blood glucose. Patients who underwent partial pancreatectomy and had preoperative HbA1c available at two academic institutions were assessed for new IDDM on discharge in relation to complication rates and survival. Of the 267 patients analyzed, 67% had abnormal HbA1c levels prior to surgery (mean 6.8%, glucose 135 mg/dL). Two hundred eight (77.9%) were not insulin-dependent prior to surgery, and 35 (16.8%) developed new IDDM after resection. On multivariable regression, increasing HbA1c and preoperative glucose were the only significant predictors for new IDDM. Optimal predictive cutoffs (HbA1c of 6.25% and glucose of 121 mg/dL) were determined in a discovery group (n = 143) and confirmed in a validation group (n = 124) with a diagnostic sensitivity of 72.7% and specificity of 84.8%. Patients with new IDDM after resection had higher rates of severe complications (OR 3.39), increased TPN at discharge (OR 4.32), and increased rates of discharge to nursing facilities (OR 2.57) (all P < 0.05). New IDDM was also associated with a decreased cancer-specific survival. Preoperative HbA1c ≥ 6.25% and blood glucose ≥ 121 mg/dL can accurately identify patients at increased risk of IDDM. These diagnostics may help identify patients in a preoperative setting that may benefit from interventions such as diabetes education or enhanced glucose control preoperatively.

To determine the glycaemic control and associated factors among patients with type-2 diabetes mellitus on tiered metformin monotherapy over one-year.Adult Asian patients on metformin monotherapy with tiered dosage up-titration (low 500 mg/day; medium 500-1000 mg/day and high ≥ 1000 mg/day) are divided into four sub-cohorts based on their baseline HbA1c 7%(CAmong 5503 eligible patients (mean age = 64.9 years, 45.6% males and 74.6% Chinese), the HbA1c absolute reduction after the up-titration at three months are 0%, 0.4%-0.6%, 0.8%-1.2% and 2.0%-2.1% for CThe results show that the baseline HbA1c and tiered metformin dosage up-titration are associated with disproportionate HbA1c reduction, time to glycaemic control and time from glycaemic control to failure.

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.

Patient similarity analytics has emerged as an essential tool to identify cohorts of patients who have similar clinical characteristics to some specific patient of interest. In this study, we propose a patient similarity measure called D3K that incorporates domain knowledge and data-driven insights. Using the electronic health records (EHRs) of 169,434 patients with either diabetes, hypertension or dyslipidaemia (DHL), we construct patient feature vectors containing demographics, vital signs, laboratory test results, and prescribed medications. We discretize the variables of interest into various bins based on domain knowledge and make the patient similarity computation to be aligned with clinical guidelines. Key findings from this study are: (1) D3K outperforms baseline approaches in all seven sub-cohorts, (2) our domain knowledge-based binning strategy outperformed the traditional percentile-based binning in all seven sub-cohorts, (3) there is substantial agreement between D3K and physicians (κ = 0.746), indicating that D3K can be applied to facilitate shared decision making. This is the first study to use patient similarity analytics on a cardiometabolic syndrome-related dataset sourced from medical institutions in Singapore. We consider patient similarity among patient cohorts with the same medical conditions to develop localized models for personalized decision support to improve the outcomes of a target patient.

Early drain removal when postoperative day (POD) one drain fluid amylase (DFA) was ≤5000 U/L reduced complications in a previous randomized controlled trial. We hypothesized that most surgeons continue to remove drains late and this is associated with inferior outcomes.We assessed the practice of surgeons in a prospectively maintained pancreas surgery registry to determine the association between timing of drain removal with demographics, comorbidities, and complications. We selected patients with POD1 DFA ≤5000 U/L and excluded those without drains, and subjects without data on POD1 DFA or timing of drain removal. Early drain removal was defined as ≤ POD5.Two hundred and forty four patients met inclusion criteria. Only 90 (37%) had drains removed early. Estimated blood loss was greater in the late removal group (190 mL versus 100 mL, P = 0.005) and pathological findings associated with soft gland texture were more frequent (97 [63%] versus 35 [39%], P 0.0001). Patients in the late drain removal group had more complications (84 [55%] versus 30 [33%], P = 0.001) including pancreatic fistula (55 [36%] versus 4 [4%], P 0.0001), delayed gastric emptying (27 [18%] versus 3 [3%], P = 0.002), and longer length of stay (7 d versus 5 d, P 0.0001). In subset analysis for procedure type, complications and pancreatic fistula remained significant for both pancreatoduodenectomy and distal pancreatectomy.Despite level one data suggesting improved outcomes with early removal when POD1 DFA is ≤ 5000 U/L, experienced pancreas surgeons more frequently removed drains late. This practice was associated with known risk factors (estimated blood loss, soft pancreas) and may be associated with inferior outcomes suggesting potential for improvement.

e23561 Background: TGCT/PVNS is an uncommon benign but locally aggressive tumor that affects joints and can lead to significant morbidity. It is characterized in most cases by a t(1;2) translocation that leads to overexpression of colony-stimulating factor 1 (CSF-1) and the accumulation of inflammatory cells that express CSF-1 receptor (CSF-1R) in the affected joints. Imatinib inhibits the tyrosine kinase activity of CSF-1R. Methods: We retrospectively reviewed patients with TGCT/PVNS who received treatment with imatinib within Kaiser Permanente Northern California. Results: Nine patients with relapsed TGCT/PVNS were treated with imatinib from 2019 to 2022. Median age was 43 years. 6 patients were female. The imatinib dose was 400 mg daily. All patients had been treated with surgery previously and relapsed, and further surgery was considered not to be beneficial. All patients presented with persistent joint pain that affected joint mobility and walking. After imatinib therapy was initiated, 7 patients (78%) reported significant improvement in their symptoms, while 2 patients (22%) reported no significant changes. Improvement in symptoms occurred soon after starting therapy, ranging from 3 days to 5 weeks. 8 patients (89%) had stable disease by RECIST criteria based on MRI, and 1 patient was not evaluable. 5 patients are still receiving imatinib currently. The longest treatment duration is 27 months. 4 patients required a dose reduction secondary to toxicity, and 3 patients discontinued therapy (2 patients with G3/4 hepatotoxicity and 1 patient self-discontinued treatment). 2 patients who discontinued imatinib continue to experience resolution of joint pain and other symptoms 10 and 24 months later respectively. Conclusions: Our study shows that treatment with imatinib can result in rapid and lasting symptomatic improvement in patients with relapsed TGCT/PVNS, even after treatment discontinuation. This suggests that imatinib can be an effective and safe treatment option for patients with relapsed TGCT/PVNS refractory or not amenable to further surgery.

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.

Background: In Singapore, the number of hip fractures per year is projected to increase from 2500 to 9000 by 2050. Psychological factors are increasingly recognised as important predictors of healthy aging. While there are a growing number of studies examining resilience in different populations, there is a paucity of literature examining psychological resilience in individuals with hip fractures. Objectives: We aim to identify the association between high psychological resilience and improved functional outcomes in post-operative hip fracture patients in a Singapore Community Hospital, defined by a Conner-Davidson Resilience Scale 25 (CD-RISC 25) score of 62 and above. Methods: We conducted a pilot prospective cohort study in Bright Vision Community Hospital in Singapore. Post-operative elderly hip fracture patients were invited to participate in the study. Psychological resilience scores were measured on Day 1 of admission using CD-RISC 25. Functional outcomes were then tracked weekly for 21 days while participants underwent rehabilitation using the Modified Barthel Index (MBI). Results: High psychological resilience was found in 52.40% of the post-operative hip fracture participants in this study. Of those participants with high psychological resilience, 55% achieved functional independence ( n = 11), compared with 47.47% of participants with low psychological resilience ( n = 9) – although this result did not achieve statistical significance. Conclusion: One in two post-operative hip fracture patients had high psychological resilience, and our study suggests that patients with high psychological resilience can possibly achieve higher gains in functional scores and achieve functional independence.

The overall purpose of this chapter will be to broadly explore both the existing and possible implementations of artificial intelligence (AI) in healthcare. The scope of this chapter will be explored from the unique perspectives of various stakeholders in the healthcare industry, namely the healthcare providers, patients, pharmaceutical companies, healthcare financial institutions, and policymakers. The chapter will seek to identify the potential benefits and pitfalls that faced by these stakeholders in implementing the use of AI, from the molecular level to a macroeconomics level; as well as seeking to understand the legal, professional, and ethical boundaries of the medical domain that are challenged as AI increasingly becomes irreversibly intertwined with the practice of medicine.

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., Author summary Bovine spongiform encephalopathy (BSE), also called “mad cow disease,” is a deadly neurodegenerative disease in cattle. BSE is caused by PrPSc, which is an aberrantly folded conformer of a normal protein in the host. PrPSc is an infectious protein and also referred to as a prion. BSE prions exist in three variants or strains: C-type BSE prions, which caused the epizootic “mad cow disease” outbreak, and two atypical forms L-type and H-type BSE prions, named according to their migration patterns during gel electrophoresis. For our investigations, we isolated L-type BSE prions from transgenic mouse brains and analyzed these samples using transmission electron microscopy and three-dimensional reconstruction techniques. Our study revealed that L-type BSE prions assemble into one- and two-protofilament containing amyloid fibrils and that the width of the two-protofilament fibrils is approximately twice that of one-protofilament fibrils. In addition, we labeled the L-type BSE fibrils at the ultrastructural level using specific anti-prion protein antibodies that recognize epitopes at both ends of the molecule. Our data agree with the previously proposed four-rung β-solenoid model for the structure of infectious PrPSc.

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.

Pancreatic diseases have long been associated with impaired glucose control. This study sought to identify the incidence of new insulin-dependent diabetes mellitus (IDDM) after pancreatectomy and the predictive accuracy of hemoglobin A1c (HbA1c) or blood glucose.Patients who underwent partial pancreatectomy and had preoperative HbA1c available at two academic institutions were assessed for new IDDM on discharge in relation to complication rates and survival.Of the 267 patients analyzed, 67% had abnormal HbA1c levels prior to surgery (mean 6.8%, glucose 135 mg/dL). Two hundred eight (77.9%) were not insulin-dependent prior to surgery, and 35 (16.8%) developed new IDDM after resection. On multivariable regression, increasing HbA1c and preoperative glucose were the only significant predictors for new IDDM. Optimal predictive cutoffs (HbA1c of 6.25% and glucose of 121 mg/dL) were determined in a discovery group (n = 143) and confirmed in a validation group (n = 124) with a diagnostic sensitivity of 72.7% and specificity of 84.8%. Patients with new IDDM after resection had higher rates of severe complications (OR 3.39), increased TPN at discharge (OR 4.32), and increased rates of discharge to nursing facilities (OR 2.57) (all P0.05). New IDDM was also associated with a decreased cancer-specific survival.Preoperative HbA1c ≥ 6.25% and blood glucose ≥ 121 mg/dL can accurately identify patients at increased risk of IDDM. These diagnostics may help identify patients in a preoperative setting that may benefit from interventions such as diabetes education or enhanced glucose control preoperatively.

BACKGROUND Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing. OBJECTIVE This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs. METHODS Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form. RESULTS A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage. CONCLUSIONS This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space. CLINICALTRIAL

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

BackgroundEarly warning scores (EWS) have been developed as clinical prognostication tools to identify acutely deteriorating patients. In the past few years, there has been a proliferation of studies that describe the development and validation of novel machine learning-based EWS. Systematic reviews of published studies which focus on evaluating performance of both well-established and novel EWS have shown conflicting conclusions. A possible reason is the heterogeneity in validation methods applied. In this review, we aim to examine the methodologies and metrics used in studies which perform EWS validation.MethodsA systematic review of all eligible studies from the MEDLINE database and other sources, was performed. Studies were eligible if they performed validation on at least one EWS and reported associations between EWS scores and inpatient mortality, intensive care unit (ICU) transfers, or cardiac arrest (CA) of adults. Two reviewers independently did a full-text review and performed data abstraction by using standardized data-worksheet based on the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. Meta-analysis was not performed due to heterogeneity.ResultsThe key differences in validation methodologies identified were (1) validation dataset used, (2) outcomes of interest, (3) case definition, time of EWS use and aggregation methods, and (4) handling of missing values. In terms of case definition, among the 48 eligible studies, 34 used the patient episode case definition while 12 used the observation set case definition, and 2 did the validation using both case definitions. Of those that used the patient episode case definition, 18 studies validated the EWS at a single point of time, mostly using the first recorded observation. The review also found more than 10 different performance metrics reported among the studies.ConclusionsMethodologies and performance metrics used in studies performing validation on EWS were heterogeneous hence making it difficult to interpret and compare EWS performance. Standardizing EWS validation methodology and reporting can potentially address this issue.

Many studies have quantified carbon emissions from a subset of cities in a nation, while few studies have estimated emissions from all cities in a nation to assess their collective contributions towards national total. This paper focuses on China and assesses the collective contribution of all cities to national carbon emissions, the share of carbon emissions by city types, and carbon emission per capita and per GDP. This paper describes the Chinese City Industrial-Infrastructure database including fuel/electricity use and heat supply in 644 cities, in which energy use is aligned with national data with ∼1% difference. It is found that direct carbon emissions from 644 Chinese cities collectively contribute to 62.4% of the national CO2 emissions. Further categorizing these cities based on population size, economic structure (Highly-Industrial, Highly-Commercial, and Mixed-Economy cities), and administrative level, it is found that Midsize cities (0.5–3 million) accounted for 38.1% of national CO2 emissions; Mixed-Economy cities contributed to about 40% of the national CO2 emissions; and city propers (all urban administrative districts in a city) collectively contribute to 42.9% of the national CO2 emissions. Direct emissions per capita ranged from 0.94 to 83.3 tonnes CO2 per person (8.85 tonnes/person on average). Direct emissions per GDP ranged from 0.01 to 2.60 kg CO2 per yuan-GDP (0.26 kg CO2/yuan-GDP on average). Direct plus embedded emissions in electricity were also evaluated and found to have similar patterns as direct carbon emissions. These results enhance our understanding of the share of carbon emissions from Chinese cities and suggest the importance of focusing on certain city types for mitigation efforts.

Cities offer unique strategies to reduce fossil fuel use through the exchange of energy and materials across homes, businesses, infrastructure and industries co-located in urban areas. However, the large-scale impact of such strategies has not been quantified. Using new models and data sets representing 637 Chinese cities, we find that such cross-sectoral strategies—enabled by compact urban design and circular economy policies—contribute an additional 15%–36% to national CO2 mitigation, compared to conventional single-sector strategies. As a co-benefit, ∼25,500 to ∼57,500 deaths annually are avoided from air pollution reduction. The benefits are highly variable across cities, ranging from

Introduction: Anxiety and depression are associated with poor asthma outcomes and increased healthcare utilization in severe asthma. However, stigma is a significant obstacle to the timely diagnosis and treatment of these conditions. Aim: To evaluate the prevalence of anxiety (A) and depression (D) symptoms, and their impact on asthma-related outcomes in severe asthma (Step 4 or 5 of GINA). Methods: 113 patients on follow up with the severe asthma clinic at Singapore General Hospital were assessed with the Hospital Anxiety and Depression Scale (HADS). Sociodemographic and clinical data including asthma-related outcomes (Asthma Control Test (ACT) scores, exacerbation rates, spirometry) were collected. Results: Mean age was 49 years and 38.9% were male. The prevalence of anxiety/depression was 46.9% in this cohort, of which only 13.2% and 9.4% had been formally diagnosed with anxiety and depression respectively. Mean ACT scores were lower in patients with positive HADS scores compared to those without (15.7 ± 4.2 vs. 19.4 ± 3.2, p Conclusions: These findings suggest that more emphasis should be placed on identifying obstacles to psychological assessment, one of which is social stigma. Systematic efforts are needed to engender improved access to psychological evaluation in patients with severe asthma. Reducing stigma and discrimination toward psychiatric afflictions is a first step.

Background Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing. Objective This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs. Methods Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form. Results A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage. Conclusions This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space.

With massive urbanization and infrastructure investments occurring in China, understanding GHG emissions from infrastructure use in small and large Chinese cities with different administrative levels is important for building future low-carbon cities. This paper identifies diverse data sources to assess GHG emission from community-wide infrastructure footprints (CIF) in four Chinese cities of varying population (1 to 20 million people) and administrative levels: Yixing, Qinhuangdao, Xiamen and Beijing. CIF addresses seven infrastructure sectors providing energy (fuels/coal), electricity, water supply and wastewater treatment, transportation, municipal waste management, construction materials, and food to support urban activities. Industrial energy use dominates the infrastructure GHG CIF in all four cities, ranging from 76% of total CIF in Yixing to 30% in Beijing, followed by residential energy use (6–13%), transportation (4–12%), commercial energy use (2–25%), food (6–11%), cement use (3–8%) and...

A circumcising incision to deglove the penis for penile prosthesis (PP) implantation can increase the risk of ischemic injury to the glans penis. In order to avoid vascular complications, we describe a novel technique utilizing a ventral incision to perform the PP implantation and a double-dorsal patch graft, or “sliding technique” (ST), in patients with severe Peyronie's disease (PD). Three patients with severe PD and erectile dysfunction at our institution underwent ST and PP implantation through a ventral incision. This new approach was not only successful in facilitating the ST and PP implantation in these patients but also allowed for adequate exposure of the penile shaft with no reported loss of sensation. We also conducted a review of current literature regarding the approaches for PD. While ischemic complications of PP implantation and ST are rare, there are reports of ischemic injury in patients undergoing a circumcising incision. The combination of a circumcising incision and a patient's underlying peripheral artery disease potentially raises a patient's risk of this rare complication. Our innovative ventral incision provides an alternative method for PP implantation and ST in order to avoid ischemia of the penis, while still allowing for adequate exposure.

The objective of this study was to test the hypothesis that distal pancreatectomy (DP) without intraperitoneal drainage does not affect the frequency of grade 2 or higher grade complications.The use of routine intraperitoneal drains during DP is controversial. Prior to this study, no prospective trial focusing on DP without intraperitoneal drainage has been reported.Patients undergoing DP for all causes at 14 high-volume pancreas centers were preoperatively randomized to placement of a drain or no drain. Complications and their severity were tracked for 60 days and mortality for 90 days. The study was powered to detect a 15% positive or negative difference in the rate of grade 2 or higher grade complications. All data were collected prospectively and source documents were reviewed at the coordinating center to confirm completeness and accuracy.A total of 344 patients underwent DP with (N = 174) and without (N = 170) the use of intraperitoneal drainage. There were no differences between cohorts in demographics, comorbidities, pathology, pancreatic duct size, pancreas texture, or operative technique. There was no difference in the rate of grade 2 or higher grade complications (44% vs. 42%, P = 0.80). There was no difference in clinically relevant postoperative pancreatic fistula (18% vs 12%, P = 0.11) or mortality (0% vs 1%, P = 0.24). DP without routine intraperitoneal drainage was associated with a higher incidence of intra-abdominal fluid collection (9% vs 22%, P = 0.0004). There was no difference in the frequency of postoperative imaging, percutaneous drain placement, reoperation, readmission, or quality of life scores.This prospective randomized multicenter trial provides evidence that clinical outcomes are comparable in DP with or without intraperitoneal drainage.

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. DOI: http://dx.doi.org/10.7554/eLife.28401.001, eLife digest Treating Alzheimer’s disease and related dementias is one of the major challenges currently facing healthcare providers worldwide. A hallmark of the disease is the formation of large deposits of a specific molecule, known as amyloid beta (Aβ), in the brain. However, more and more research suggests that smaller and particularly toxic amyloid beta clumps – often referred to as oligomeric Aβ – appear as an early sign of Alzheimer’s disease. To understand how the formation of these smaller amyloid beta clumps triggers other aspects of the disease, it is important to identify molecules in the human brain that oligomeric Aβ binds to. To this end, Wang et al. attached amyloid beta or oligomeric Aβ molecules to microscopically small beads. The beads were then exposed to human brain extracts in a test tube, which allowed molecules in the extracts to bind to the amyloid beta or oligomeric Aβ. The samples were then spun at high speed, meaning that the beads and any other molecules bound to them sunk and formed pellets at the bottom of the tubes. Each pellet was then analyzed to see which molecules it contained. The experiments identified more than a hundred human brain proteins that can bind to amyloid beta. One of them, known as somatostatin, selectively binds to oligomeric Aβ. Wang et al. were able to determine the structural features of somatostatin that control this binding. Finally, in further experiments performed in test tubes, Wang et al. noticed that smaller oligomeric Aβ clumps were more likely to form than larger amyloid beta deposits when somatostatin was present. This could signify a previously unrecognized role of somatostatin in the development of Alzheimer’s disease. Further studies are now needed to confirm whether the presence of somatostatin in the brain favors the formation of smaller, toxic oligomeric Aβ clumps over large innocuous amyloid beta deposits. If so, new treatments could be developed that aim to reduce oligomeric Aβ levels in the brain by preventing somatostatin from interacting with amyloid beta molecules. Wang et al. also suggest that somatostatin could be used in diagnostic tests to detect abnormal levels of oligomeric Aβ in the brain or body fluids of people who have Alzheimer’s disease. DOI: http://dx.doi.org/10.7554/eLife.28401.002

Algae are an appealing source for bioenergy due to their high yields relative to terrestrial energy crops. The high cost of production, however, has prohibited commercialization. Hydrothermal liquefaction is a technology that converts more of the algae into oil than alternative technologies, thereby reducing the amount of expensive pond infrastructure and energy required for cultivation. We incorporate recent experimental results into an analysis that models the economic and life cycle performance of an algal biorefinery across a range of reaction conditions. Two strategies are explored: one pathway with gasification of the aqueous waste products for onsite energy recovery and another pathway featuring cultivation of Escherichia coli on the aqueous products and recycling of the biomass back through the reactor for boosted oil yields. We found that the maximum net energy ratio of 1.9 and minimum global warming potential of 1.0 kg CO2e L-oil–1 occurred with gasification, along with the minimum reaction temp...

Cities are setting both sustainability and resilience goals that recognize the significant pressures that cities will face over the coming decades due to increasing global populations, aging infrastructure, and hazards posed by climate change. To further help cities reach and meet this broad range of goals, the World Bank recently released the Urban Sustainability Framework in early 2018 as a framework for achieving an intelligent growth scenario, with a key recommendation of calls for the more efficient use of existing infrastructure. Albeit a prudent course, the first step in adding more stress to existing infrastructure requires a baseline cross-sector examination as to the existing daily reliability, age of assets, and other vulnerabilities regarding climate change. This examination of the inherent reliability of a city’s infrastructure systems then becomes the foundation for prioritizing projects in the capital investment planning process. However, to better integrate cross-silo priorities, new key performance indicators are required to better connect existing infrastructure vulnerabilities to a city’s carbon footprint and move towards synchronizing climate action and capital investment planning priorities to better represent intelligent growth and resource use.

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 patient's 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 debridement, 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 patient's preoperative risk factors may help the physician to optimize a patient's preoperative condition. Additionally, awareness of critical intraoperative parameters will help to 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.

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.

We have implemented Low-coherence Enhanced Backscattering (LEBS) as a tool for non-invasively measuring optical properties. We observe that coral skeletons that are susceptible to bleaching have smaller reduced scattering coefficients and fractal dimensions.