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Background Neoadjuvant chemotherapy (NAC) is a standard approach for treating locally advanced gastric cancer (GC), but resistance in some patients can result in treatment toxicity and surgical delays without therapeutic benefit. Identifying biomarkers predictive of NAC response is crucial for personalized treatment strategies. This study evaluated circulating microRNAs (miRNAs) as potential biomarkers for NAC response. Materials and methods Plasma samples from 39 GC patients undergoing NAC followed by gastrectomy (NCT04223401) were collected before treatment. Four miRNAs (miR-19a, miR-21, miR-27a, miR-200c) were analyzed via quantitative real-time polymerase chain reaction. NAC response was assessed in histological specimens using the Becker tumor regression grade (TRG), which classifies patients as Responders (TRG 1–2) or Non-responders (TRG 3). Results Among 39 patients, 20 (51%) were Responders, and 19 (49%) were Non-responders. miR-19a, miR-21, and miR-200c were significantly upregulated in Non-responders (p < 0.05). ROC analysis revealed miR-19a (AUC: 0.693), miR-21 (AUC: 0.700), and miR-200c (AUC: 0.772) as predictive of resistance. Univariate analysis revealed a correlation between higher levels of miR-19a, miR-21, and miR-200c and a low neutrophil count, with increased resistance risk. Multivariate analysis confirmed miR-200c as an independent predictor of resistance (OR: 20.90; 95% CI: 1.54-283.73). Conclusions This pilot study identifies circulating miR-19a, miR-21, and miR-200c as novel biomarkers for poor NAC response in GC, providing a foundation for personalized treatment strategies.

Background: The novel coronavirus (SARS-CoV-2) led to gastrointestinal manifestations in up to 50% of cases, with diarrhea being common, and probiotics have been suggested as a potential treatment. Aim: This study aimed to assess changes in the microbiome and the effects of a multispecies probiotic in patients with COVID-19 in home quarantine through a fully remote telemedical approach. Methods: Thirty patients were randomized to receive either the Ecologic AAD probiotic (Winclove Probiotics, Amsterdam, The Netherlands), on the market as OMNi-BiOTiC 10 (Allergosan, Austria), or a placebo for 30 days in a 2:1 ratio. Respiratory and gastrointestinal symptoms were monitored in 2–10-day intervals via online surveys, and five stool samples were collected during the 30-day study period for microbiome and metabolomics analyses. Twenty-four healthy volunteers served as controls. Results: Of the 30 patients, 26 completed this study (10 placebo, 16 probiotic). Patients reported respiratory symptoms and a diminished gastrointestinal quality of life, both of which improved significantly during the study period, irrespective of the intervention. Compared to controls, infected patients showed significant alterations in the fecal microbiome (p = 0.002), including an increase in Bacteroidetes and decreases in Christensenellaceae, Ruminococcaceae, and Gammaproteobacteria, along with metabolomic changes. Probiotic treatment significantly modulated the patients’ microbiome beta diversity (p = 0.001) and introduced the Enterococcus faecium W54 strain. Symptoms, COVID-19-related taxa, and the fecal metabolome were not affected by the intervention. Conclusions: Patients with mild COVID-19 disease in home quarantine exhibited respiratory symptoms, a reduced gastrointestinal quality of life, and changes in the fecal microbiome and metabolome.

Background: The gut–lung axis could be a potential therapeutic target for improving post-acute COVID-19 symptoms, and probiotics have been proposed as possible modulators. Aim: We conducted a pilot study to understand alterations in the gut–lung axis and to explore the effects of a probiotic in post-acute COVID-19 disease. Methods: We included patients after severe COVID-19 disease (sCOV, n = 21) in a randomized, placebo-controlled trial to test the effect of a probiotic (Pro-Vi 5, Institute Allergosan, Graz, Austria) in a six-month intervention and used patients after mild disease (mCOV, n = 10) as controls, to compare the intestinal microbiome, metabolome, and patient-reported outcomes and biomarkers along the gut–lung axis at baseline and throughout probiotic intervention. Results: Compared to mCOV patients, sCOV patients showed lower microbial richness, which was significantly improved by probiotic intervention. A reorganization of Ruminococcaceae and Lachnospiraceae taxa was observed in sCOV patients but remained unaffected by the intervention. Serum metabolome showed a dysregulation of lipoproteins in accordance with higher BMI and comorbidities in sCOV patients. HDL and LDL fractions/components were temporarily decreased in the probiotic group. Stool metabolome was altered at baseline in sCOV patients and an increase in L-DOPA after 3 months and butyrate after 6 months of intervention could be observed. Probiotics partially improved reduced quality of life and modulated altered immune responses in sCOV patients. Increased intestinal permeability at baseline remained unaffected. Conclusion: The study provides evidence of long-term alterations of the gut–lung axis after severe COVID-19 infection and suggests that probiotics can modulate the biomarkers of the gut–lung axis.

The ABC and ACMG variant classification systems were compared by asking mainly European clinical laboratories to classify variants in 10 challenging cases using both systems, and to state if the variant in question would be reported as a relevant result or not as a measure of clinical utility. In contrast to the ABC system, the ACMG system was not made to guide variant reporting but to determine the likelihood of pathogenicity. Nevertheless, this comparison is justified since the ACMG class determines variant reporting in many laboratories. Forty-three laboratories participated in the survey. In seven cases, the classification system used did not influence the reporting likelihood when variants labeled as “maybe report” after ACMG-based classification were included. In three cases of population frequent but disease-associated variants, there was a difference in favor of reporting after ABC classification. A possible reason is that ABC step C (standard variant comments) allows a variant to be reported in one clinical setting but not another, e.g., based on Bayesian-based likelihood calculation of clinical relevance. Finally, the selection of ACMG criteria was compared between 36 laboratories. When excluding criteria used by less than four laboratories (<10%), the average concordance rate was 46%. Taken together, ABC-based classification is more clear-cut than ACMG-based classification since molecular and clinical information is handled separately, and variant reporting can be adapted to the clinical question and phenotype. Furthermore, variants do not get a clinically inappropriate label, like pathogenic when not pathogenic in a clinical context, or variant of unknown significance when the significance is known.

The ABC and ACMG variant classification systems were compared by asking mainly European clinical laboratories to classify variants in 10 challenging cases using both systems, and to state if the variant in question would be reported as a relevant result or not as a measure of clinical utility. In contrast to the ABC system, the ACMG system was not made to guide variant reporting but to determine the likelihood of pathogenicity. Nevertheless, this comparison is justified since the ACMG class determines variant reporting in many laboratories. Forty-three laboratories participated in the survey. In seven cases, the classification system used did not influence the reporting likelihood when variants labeled as “maybe report” after ACMG-based classification were included. In three cases of population frequent but disease-associated variants, there was a difference in favor of reporting after ABC classification. A possible reason is that ABC step C (standard variant comments) allows a variant to be reported in one clinical setting but not another, e.g., based on Bayesian-based likelihood calculation of clinical relevance. Finally, the selection of ACMG criteria was compared between 36 laboratories. When excluding criteria used by less than four laboratories (<10%), the average concordance rate was 46%. Taken together, ABC-based classification is more clear-cut than ACMG-based classification since molecular and clinical information is handled separately, and variant reporting can be adapted to the clinical question and phenotype. Furthermore, variants do not get a clinically inappropriate label, like pathogenic when not pathogenic in a clinical context, or variant of unknown significance when the significance is known.

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.