Antimicrobial Potency of Fmoc-Phe-Phe Dipeptide Hydrogels with Encapsulated Porphyrin Chromophores Is a Promising Alternative in Antimicrobial Resistance.

Antimicrobial resistance (AMR) poses a significant global health risk as a consequence of misuse of antibiotics. Owing to the increasing antimicrobial resistance, it became imperative to develop novel molecules and materials with antimicrobial properties. Porphyrins and metalloporphyrins are compounds which present antimicrobial properties especially after irradiation. As a consequence, porphyrinoids have recently been utilized as antimicrobial agents in antimicrobial photodynamic inactivation in bacteria and other microorganisms. Herein, we report the encapsulation of porphyrins into peptide hydrogels which serve as delivery vehicles. We selected the self-assembling Fmoc-Phe-Phe dipeptide, a potent gelator, as a scaffold due to its previously reported biocompatibility and three different water-soluble porphyrins as photosensitizers. We evaluated the structural, mechanical and in vitro degradation properties of these hydrogels, their interaction with NIH3T3 mouse skin fibroblasts, and we assessed their antimicrobial efficacy against Gram-positive Staphylococcus aureus (S. aureus) and Gram-negative Escherichia coli (E. coli) bacteria. We found out that the hydrogels are cytocompatible and display antimicrobial efficiency against both strains with the zinc porphyrins being more efficient. Therefore, these hydrogels present a promising alternative for combating bacterial infections in the face of growing AMR concerns.

Antenna Effect in Noble Metal-Free Dye-Sensitized Photocatalytic Systems Enhances CO2 -to-CO Conversion.

Dye-sensitized photocatalytic systems (DSPs) have been extensively investigated for solar-driven hydrogen (H2 ) evolution. However, their application in carbon dioxide (CO2 ) reduction remains limited. Furthermore, current solar-driven CO2 -to-CO DSPs typically employ rhenium complexes as catalysts. In this study, we have developed DSPs that incorporate noble metal-free components, specifically a zinc-porphyrin as photosensitizer (PS) and a cobalt-quaterpyridine as catalyst (CAT). Taking a significant stride forward, we have achieved an antenna effect for the first time in CO2 -to-CO DSPs by introducing a Bodipy as an additional chromophore to enhance light harvesting efficiency. The energy transfer from Bodipy to zinc porphyrin resulted in remarkable stability (turn over number (TON)=759 vs. CAT), and high CO evolution activity (42 mmol g-1 h-1 vs. CAT).

Dye-Sensitized Photocatalysis: Hydrogen Evolution and Alcohol-to-Aldehyde Oxidation without Sacrifical Electron Donor.

There is a growing interest in developing dye-sensitized photocatalytic systems (DSPs) to produce molecular hydrogen (H2 ) as alternative energy source. To improve the sustainability of this technology, we replaced the sacrificial electron donor (SED), typically an expensive and polluting chemical, with an alcohol oxidation catalyst. This study demonstrates the first dye-sensitized system using a diketopyrrolopyrrole dye covalently linked to 2,2,6,6-tetramethyl-1-piperidine-N-oxyl (TEMPO) based catalyst for simultaneous H2 evolution and alcohol-to-aldehyde transformation operating in water with visible irradiation.

Highly efficient light-driven hydrogen evolution utilizing porphyrin-based nanoparticles.

We developed dye-sensitized photocatalytic systems (DSPs) by utilizing porphyrins as a photosensitizer (PS) or as a photosensitizer-catalyst (PS/CAT) upon their chemisorption onto platinum-doped titanium dioxide nanoparticles (Pt-TiO2 NPs). The DSPs coated with Pt-Tc3CP (PS/CAT entity) exhibited a record-high stability (25 500 TONs) and H2 evolution activity (707 mmol g-1 h-1) compared to similar DSPs in the literature.

Delivery of Porphyrins Through Self-Assembling Peptide Hydrogels for Accelerated Healing of Experimental Skin Defects In Vivo.

INTRODUCTION: The care and healing of skin defects resulting from different causes has been the object of research to achieve rapid and complete skin regeneration. Hydrogels have been used for their ability to maintain hydration during wound healing, absorb wound exudate, and cover the underlying tissue without adherence while being transparent. In this study, we evaluated the efficacy of a hydrogel (H) with encapsulated porphyrin (H+P) on a rat model of surgically-induced skin defects. METHODS: Four round 6 mm diameter skin defects were performed under general anesthesia on the dorsal area of 24 three-month-old "Young" and 24 twelve-month-old "Mature" male rats. Each age group was separated into the Control, H, and H+P groups, n=8 each, where no therapy, H, or H+P was respectively applied daily for 20 days. Digital photographs and skin biopsies were taken on the third, seventh, 10th, and 20th postoperative days and evaluated by planimetry, histology, and immunohistochemistry. RESULTS: Planimetry results demonstrated significantly decreased perimeter, diameter, and area measurements (p<0.005) of group H+P compared to Control and H groups on days 10 and 20 in the young rats, while in the mature rats, the significant differences were evident earlier (perimeter third day p<0.05; diameter and area seventh day p<0.05 and p<0.005, respectively vs. H). Granulation and scar tissue formation were also reduced in the H+P groups although they were not statistically significant. CONCLUSIONS: The application of H+P on the skin defects benefited the healing process in both young and mature animal groups, as evidenced by the statistically significant findings of planimetry. The beneficial healing process was more pronounced in the mature animals, both in the level of statistical significance as well as regarding time (evident already on the third day of healing), probably due to porphyrin assisting the reduced healing rate, which is observed in organisms of advanced age.

Tandem electrocatalytic CO2 reduction with Fe-porphyrins and Cu nanocubes enhances ethylene production.

Copper-based tandem schemes have emerged as promising strategies to promote the formation of multi-carbon products in the electrocatalytic CO2 reduction reaction. In such approaches, the CO-generating component of the tandem catalyst increases the local concentration of CO and thereby enhances the intrinsic carbon-carbon (C-C) coupling on copper. However, the optimal characteristics of the CO-generating catalyst for maximizing the C2 production are currently unknown. In this work, we developed tunable tandem catalysts comprising iron porphyrin (Fe-Por), as the CO-generating component, and Cu nanocubes (Cucub) to understand how the turnover frequency for CO (TOFCO) of the molecular catalysts impacts the C-C coupling on the Cu surface. First, we tuned the TOFCO of the Fe-Por by varying the number of orbitals involved in the π-system. Then, we coupled these molecular catalysts with the Cucub and assessed the current densities and faradaic efficiencies. We discovered that all of the designed Fe-Por boost ethylene production. The most efficient Cucub/Fe-Por tandem catalyst was the one including the Fe-Por with the highest TOFCO and exhibited a nearly 22-fold increase in the ethylene selectivity and 100 mV positive shift of the onset potential with respect to the pristine Cucub. These results reveal that coupling the TOFCO tunability of molecular catalysts with copper nanocatalysts opens up new possibilities towards the development of Cu-based catalysts with enhanced selectivity for multi-carbon product generation at low overpotential.

Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response.

BACKGROUND: Theranostic approaches-the use of diagnostics for developing targeted therapies-are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments. METHODS: We performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study 'Understanding Society'. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response. RESULTS: Our analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function. CONCLUSIONS: These findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases.

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network.

PURPOSE: Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. METHODS: We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model's performance. From the external test dataset, we calculated the model's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. RESULTS: Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. CONCLUSIONS: Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00166-4.

Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the 'fast decliner' phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis. METHODS: A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set). RESULTS: Three COPD phenotypes were identified, the most common of which was the 'fast decliner'-characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function-yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype. CONCLUSIONS: In this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.

Mini-Cog for the detection of dementia within a community setting.

BACKGROUND: Alzheimer's disease and related forms of dementia are becoming increasingly prevalent with the aging of many populations. The diagnosis of Alzheimer's disease relies on tests to evaluate cognition and discriminate between individuals with dementia and those without dementia. The Mini-Cog is a brief, cognitive screening test that is frequently used to evaluate cognition in older adults in various settings. OBJECTIVES: The primary objective of this review was to determine the accuracy of the Mini-Cog for detecting dementia in a community setting. Secondary objectives included investigations of the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity included the baseline prevalence of dementia in study samples, thresholds used to determine positive test results, the type of dementia (Alzheimer's disease dementia or all causes of dementia), and aspects of study design related to study quality. Overall, the goals of this review were to determine if the Mini-Cog is a cognitive screening test that could be recommended to screen for cognitive impairment in community settings. SEARCH METHODS: We searched MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (Ovid SP), Science Citation Index (Web of Science), BIOSIS previews (Web of Science), LILACS (BIREME), and the Cochrane Dementia Group's developing register of diagnostic test accuracy studies to March 2013. We used citation tracking (using the database's 'related articles' feature, where available) as an additional search method and contacted authors of eligible studies for unpublished data. SELECTION CRITERIA: We included all cross-sectional studies that utilized the Mini-Cog as an index test for the diagnosis of dementia when compared to a reference standard diagnosis of dementia using standardized dementia diagnostic criteria. For the current review we only included studies that were conducted on samples from community settings, and excluded studies that were conducted in primary care or secondary care settings. We considered studies to be conducted in a community setting where participants were sampled from the general population. DATA COLLECTION AND ANALYSIS: Information from studies meeting the inclusion criteria were extracted including information on the characteristics of participants in the studies. The quality of the studies was assessed using the QUADAS-2 criteria and summarized using risk of bias applicability and summary graphs. We extracted information on the diagnostic test accuracy of studies including the sensitivity, specificity, and 95% confidence intervals of these measures and summarized the findings using forest plots. Study specific sensitivities and specificities were also plotted in receiver operating curve space. MAIN RESULTS: Three studies met the inclusion criteria, with a total of 1620 participants. The sensitivities of the Mini-Cog in the individual studies were reported as 0.99, 0.76 and 0.99. The specificity of the Mini-Cog varied in the individual studies and was 0.93, 0.89 and 0.83. There was clinical and methodological heterogeneity between the studies which precluded a pooled meta-analysis of the results. Methodological limitations were present in all the studies introducing potential sources of bias, specifically with respect to the methods for participant selection. AUTHORS' CONCLUSIONS: There are currently few studies assessing the diagnostic test accuracy of the Mini-Cog in community settings. The limited number of studies and the methodological limitations that are present in the current studies make it difficult to provide recommendations for or against the use of the Mini-Cog as a cognitive screening test in community settings. Additional well-designed studies comparing the Mini-Cog to other brief cognitive screening tests are required in order to determine the accuracy and utility of the Mini-Cog in community based settings.

Mini-Cog for the detection of dementia within a secondary care setting.

BACKGROUND: The diagnosis of Alzheimer's disease dementia and other dementias relies on clinical assessment. There is a high prevalence of cognitive disorders, including undiagnosed dementia in secondary care settings. Short cognitive tests can be helpful in identifying those who require further specialist diagnostic assessment; however, there is a lack of consensus around the optimal tools to use in clinical practice. The Mini-Cog is a short cognitive test comprising three-item recall and a clock-drawing test that is used in secondary care settings. OBJECTIVES: The primary objective was to determine the accuracy of the Mini-Cog for detecting dementia in a secondary care setting. The secondary objectives were to investigate the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity will include the baseline prevalence of dementia in study samples, thresholds used to determine positive test results, the type of dementia (Alzheimer's disease dementia or all causes of dementia), and aspects of study design related to study quality. SEARCH METHODS: We searched the following sources in September 2012, with an update to 12 March 2019: Cochrane Dementia Group Register of Diagnostic Test Accuracy Studies, MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (Web of Knowledge), Science Citation Index (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We made no exclusions with regard to language of Mini-Cog administration or language of publication, using translation services where necessary. SELECTION CRITERIA: We included cross-sectional studies and excluded case-control designs, due to the risk of bias. We selected those studies that included the Mini-Cog as an index test to diagnose dementia where dementia diagnosis was confirmed with reference standard clinical assessment using standardised dementia diagnostic criteria. We only included studies in secondary care settings (including inpatient and outpatient hospital participants). DATA COLLECTION AND ANALYSIS: We screened all titles and abstracts generated by the electronic database searches. Two review authors independently checked full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool. We extracted data into two-by-two tables to allow calculation of accuracy metrics for individual studies, reporting the sensitivity, specificity, and 95% confidence intervals of these measures, summarising them graphically using forest plots. MAIN RESULTS: Three studies with a total of 2560 participants fulfilled the inclusion criteria, set in neuropsychology outpatient referrals, outpatients attending a general medicine clinic, and referrals to a memory clinic. Only n = 1415 (55.3%) of participants were included in the analysis to inform evaluation of Mini-Cog test accuracy, due to the selective use of available data by study authors. There were concerns related to high risk of bias with respect to patient selection, and unclear risk of bias and high concerns related to index test conduct and applicability. In all studies, the Mini-Cog was retrospectively derived from historic data sets. No studies included acute general hospital inpatients. The prevalence of dementia ranged from 32.2% to 87.3%. The sensitivities of the Mini-Cog in the individual studies were reported as 0.67 (95% confidence interval (CI) 0.63 to 0.71), 0.60 (95% CI 0.48 to 0.72), and 0.87 (95% CI 0.83 to 0.90). The specificity of the Mini-Cog for each individual study was 0.87 (95% CI 0.81 to 0.92), 0.65 (95% CI 0.57 to 0.73), and 1.00 (95% CI 0.94 to 1.00). We did not perform meta-analysis due to concerns related to risk of bias and heterogeneity. AUTHORS' CONCLUSIONS: This review identified only a limited number of diagnostic test accuracy studies using Mini-Cog in secondary care settings. Those identified were at high risk of bias related to patient selection and high concerns related to index test conduct and applicability. The evidence was indirect, as all studies evaluated Mini-Cog differently from the review question, where it was anticipated that studies would conduct Mini-Cog and independently but contemporaneously perform a reference standard assessment to diagnose dementia. The pattern of test accuracy varied across the three studies. Future research should evaluate Mini-Cog as a test in itself, rather than derived from other neuropsychological assessments. There is also a need for evaluation of the feasibility of the Mini-Cog for the detection of dementia to help adequately determine its role in the clinical pathway.

The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. METHODS: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. RESULTS: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was characterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. CONCLUSIONS: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

Mini-Cog for the detection of dementia within a primary care setting.

BACKGROUND: Alzheimer's disease and other forms of dementia are becoming increasingly common with the aging of most populations. The majority of individuals with dementia will first present for care and assessment in primary care settings. There is a need for brief dementia screening instruments that can accurately detect dementia in primary care settings. The Mini-Cog is a brief, cognitive screening test that is frequently used to evaluate cognition in older adults in various settings. OBJECTIVES: To determine the accuracy of the Mini-Cog for detecting dementia in a primary care setting. SEARCH METHODS: We searched the Cochrane Dementia and Cognitive Improvement Register of Diagnostic Test Accuracy Studies, MEDLINE, Embase and four other databases, initially to September 2012. Since then, four updates to the search were performed using the same search methods, and the most recent was January 2017. We used citation tracking (using the databases' 'related articles' feature, where available) as an additional search method and contacted authors of eligible studies for unpublished data. SELECTION CRITERIA: We only included studies that evaluated the Mini-Cog as an index test for the diagnosis of Alzheimer's disease dementia or related forms of dementia when compared to a reference standard using validated criteria for dementia. We only included studies that were conducted in primary care populations. DATA COLLECTION AND ANALYSIS: We extracted and described information on the characteristics of the study participants and study setting. Using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria we evaluated the quality of studies, and we assessed risk of bias and applicability of each study for each domain in QUADAS-2. Two review authors independently extracted information on the true positives, true negatives, false positives, and false negatives and entered the data into Review Manager 5 (RevMan 5). We then used RevMan 5 to determine the sensitivity, specificity, and 95% confidence intervals. We summarized the sensitivity and specificity of the Mini-Cog in the individual studies in forest plots and also plotted them in a receiver operating characteristic plot. We also created a 'Risk of bias' and applicability concerns graph to summarize information related to the quality of included studies. MAIN RESULTS: There were a total of four studies that met our inclusion criteria, including a total of 1517 total participants. The sensitivity of the Mini-Cog varied between 0.76 to 1.00 in studies while the specificity varied between 0.27 to 0.85. The included studies displayed significant heterogeneity in both methodologies and clinical populations, which did not allow for a meta-analysis to be completed. Only one study (Holsinger 2012) was found to be at low risk of bias on all methodological domains. The results of this study reported that the sensitivity of the Mini-Cog was 0.76 and the specificity was 0.73. We found the quality of all other included studies to be low due to a high risk of bias with methodological limitations primarily in their selection of participants. AUTHORS' CONCLUSIONS: There is a limited number of studies evaluating the accuracy of the Mini-Cog for the diagnosis of dementia in primary care settings. Given the small number of studies, the wide range in estimates of the accuracy of the Mini-Cog, and methodological limitations identified in most of the studies, at the present time there is insufficient evidence to recommend that the Mini-Cog be used as a screening test for dementia in primary care. Further studies are required to determine the accuracy of Mini-Cog in primary care and whether this tool has sufficient diagnostic test accuracy to be useful as a screening test in this setting.

Photocatalytic hydrogen production of porphyrin nanostructures: spheres vs. fibrils, a case study.

Herein, we illustrate the preparation of a covalent connected peptide-porphyrin hybrid (Fmoc-FF-(Zn)Por). The thorough investigation of its self-organization features demonstrated that Fmoc-FF-(Zn)Por self-assembles into either spheres or fibrils by altering the solvent mixture. Interestingly, photocatalytic hydrogen (H2) evolution experiments revealed that fibrils were more efficient towards H2 production compared to spheres.

Eculizumab discontinuation in atypical haemolytic uraemic syndrome: TMA recurrence risk and renal outcomes.

Background: Eculizumab modifies the course of disease in patients with atypical haemolytic uraemic syndrome (aHUS), but data evaluating whether eculizumab discontinuation is safe are limited. Methods: Patients enrolled in the Global aHUS Registry who received ≥1 month of eculizumab before discontinuing, demonstrated haematologic or renal response prior to discontinuation and had ≥6 months of follow-up were analysed. The primary endpoint was the proportion of patients suffering from thrombotic microangiopathy (TMA) recurrence after eculizumab discontinuation. Additional endpoints included: estimated glomerular filtration rate changes following eculizumab discontinuation to last available follow-up; number of TMA recurrences; time to TMA recurrence; proportion of patients restarting eculizumab; and changes in renal function. Results: We analysed 151 patients with clinically diagnosed aHUS who had evidence of haematologic or renal response to eculizumab, before discontinuing. Thirty-three (22%) experienced a TMA recurrence. Univariate analysis revealed that patients with an increased risk of TMA recurrence after discontinuing eculizumab were those with a history of extrarenal manifestations prior to initiating eculizumab, pathogenic variants or a family history of aHUS. Multivariate analysis showed an increased risk of TMA recurrence in patients with pathogenic variants and a family history of aHUS. Twelve (8%) patients progressed to end-stage renal disease after eculizumab discontinuation; seven (5%) patients eventually received a kidney transplant. Forty (27%) patients experienced an extrarenal manifestation of aHUS after eculizumab discontinuation. Conclusions: Eculizumab discontinuation in patients with aHUS is not without risk, potentially leading to TMA recurrence and renal failure. A thorough assessment of risk factors prior to the decision to discontinue eculizumab is essential.

Controlling Solar Hydrogen Production by Organizing Porphyrins.

In this study, a highly efficient photocatalytic H2 production system is developed by employing porphyrins as photocatalysts. Palladium and platinum tetracarboxyporphyrins (PdTCP and PtTCP) are adsorbed or coadsorbed onto TiO2 nanoparticles (NPs), which act as the electron transport medium and as a scaffold that promotes the self-organization of the porphyrinoids. The self-organization of PdTCP and PtTCP, forming H- and J-aggregates, respectively, is the key element for H2 evolution, as in the absence of TiO2 NPs no catalytic activity is detected. Notably, J-aggregated PtTCPs are more efficient for H2 production than H-aggregated PdTCPs. In this approach, a single porphyrin, which self-organizes onto TiO2 NPs, acts as the light harvester and simultaneously as the catalyst, whereas TiO2 serves as the electron transport medium. Importantly, the concurrent adsorption of PdTCP and PtTCP onto TiO2 NPs results in the most efficient catalytic system, giving a turnover number of 22,733 and 30.2 mmol(H2 ) g(cat)-1 .

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda.

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

Photosensitizers for H2 Evolution Based on Charged or Neutral Zn and Sn Porphyrins.

We report a comparison between a series of zinc and tin porphyrins as photosensitizers for photochemical hydrogen evolution using cobaloxime complexes as molecular catalysts. Among all the chromophores tested, only the positively charged zinc porphyrin, [ZnTMePyP4+]Cl4, and the neutral tin porphyrin derivatives, Sn(OH)2TPyP, Sn(Cl2)TPP-[COOMe]4, and Sn(Cl2)TPP-[PO(OEt)2]4, were photocatalytically active. Hydrogen evolution was strongly affected by the pH value as well as the different concentrations of both the sensitizer and the catalyst. A comprehensive photophysical and electrochemical investigation was conducted in order to examine the mechanism of photocatalysis. The results derived from this study establish fundamental criteria with respect to the design and synthesis of porphyrin derivatives for their application as photosensitizers in photoinduced hydrogen evolution.

Self-assembly of aliphatic dipeptides coupled with porphyrin and BODIPY chromophores.

In this work, the self assembly ability of chromophores covalently linked to aliphatic dipeptides is described. Altering various parameters such as the protecting group, the solvent mixture, the dipeptide and the chromophore resulted in different nanostructures. Interestingly, a peptide-porphyrin hybrid is capable of forming a hydrogel in HFIP-water solvent mixture.

Mini-Cog for the diagnosis of Alzheimer's disease dementia and other dementias within a secondary care setting.

BACKGROUND: The diagnosis of Alzheimer's disease dementia and other dementias relies on clinical assessment. There is a high prevalence of cognitive disorders, including undiagnosed dementia in secondary care settings. Short cognitive tests can be helpful in identifying those who require further specialist diagnostic assessment; however, there is a lack of consensus around the optimal tools to use in clinical practice. The Mini-Cog is a short cognitive test comprising three-item recall and a clock-drawing test that is used in secondary care settings. OBJECTIVES: The primary objective was to determine the diagnostic accuracy of the Mini-Cog for detecting Alzheimer's disease dementia and other dementias in a secondary care setting. The secondary objectives were to investigate the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity will include the baseline prevalence of dementia in study samples, thresholds used to determine positive test results, the type of dementia (Alzheimer's disease dementia or all causes of dementia), and aspects of study design related to study quality. SEARCH METHODS: We searched the following sources in September 2012, with an update to 12 March 2019: Cochrane Dementia Group Register of Diagnostic Test Accuracy Studies, MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (Web of Knowledge), Science Citation Index (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We made no exclusions with regard to language of Mini-Cog administration or language of publication, using translation services where necessary. SELECTION CRITERIA: We included cross-sectional studies and excluded case-control designs, due to the risk of bias. We selected those studies that included the Mini-Cog as an index test to diagnose dementia where dementia diagnosis was confirmed with reference standard clinical assessment using standardised dementia diagnostic criteria. We only included studies in secondary care settings (including inpatient and outpatient hospital participants). DATA COLLECTION AND ANALYSIS: We screened all titles and abstracts generated by the electronic database searches. Two review authors independently checked full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool. We extracted data into two-by-two tables to allow calculation of accuracy metrics for individual studies, reporting the sensitivity, specificity, and 95% confidence intervals of these measures, summarising them graphically using forest plots. MAIN RESULTS: Three studies with a total of 2560 participants fulfilled the inclusion criteria, set in neuropsychology outpatient referrals, outpatients attending a general medicine clinic, and referrals to a memory clinic. Only n = 1415 (55.3%) of participants were included in the analysis to inform evaluation of Mini-Cog test accuracy, due to the selective use of available data by study authors. There were concerns related to high risk of bias with respect to patient selection, and unclear risk of bias and high concerns related to index test conduct and applicability. In all studies, the Mini-Cog was retrospectively derived from historic data sets. No studies included acute general hospital inpatients. The prevalence of dementia ranged from 32.2% to 87.3%. The sensitivities of the Mini-Cog in the individual studies were reported as 0.67 (95% confidence interval (CI) 0.63 to 0.71), 0.60 (95% CI 0.48 to 0.72), and 0.87 (95% CI 0.83 to 0.90). The specificity of the Mini-Cog for each individual study was 0.87 (95% CI 0.81 to 0.92), 0.65 (95% CI 0.57 to 0.73), and 1.00 (95% CI 0.94 to 1.00). We did not perform meta-analysis due to concerns related to risk of bias and heterogeneity. AUTHORS' CONCLUSIONS: This review identified only a limited number of diagnostic test accuracy studies using Mini-Cog in secondary care settings. Those identified were at high risk of bias related to patient selection and high concerns related to index test conduct and applicability. The evidence was indirect, as all studies evaluated Mini-Cog differently from the review question, where it was anticipated that studies would conduct Mini-Cog and independently but contemporaneously perform a reference standard assessment to diagnose dementia. The pattern of test accuracy varied across the three studies. Future research should evaluate Mini-Cog as a test in itself, rather than derived from other neuropsychological assessments. There is also a need for evaluation of the feasibility of the Mini-Cog for the diagnosis of dementia to help adequately determine its role in the clinical pathway.