Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R

Baseline urinary osteopontin levels are associated with the improvement of metabolic syndrome

BACKGROUND AND AIMS

While serum osteopontin (OPN)'s established role in cardiometabolic risk is recognized, its potential as a predictor of metabolic syndrome (MetS) improvement through a urine assay has not yet been demonstrated. In this study, we propose its potential predictive role over a 12-month period of standard care, with the ability to complement anthropometric measures.

METHODS AND RESULTS

Hierarchical clustering revealed a notable association of urinary OPN (uOPN) with MetS criteria and overcame anthropometric measures in predicting the improvement at 12 months (OR of 2.74 [95% CI 1.32 to 6.29]). uOPN significantly contributed to the homogeneity of the nodes in the random forest and ultimately enhanced the performance of anthropometric measures when assessed for accuracy and area under the curve (AUC).

CONCLUSION

Our findings offer insights into potential applications in cardiometabolic medicine for uOPN, which is easily detectable in non-invasive biological samples through an affordable assay.

Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research

With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.

Time-varying similarity of neural responses to musical tension is shaped by physical features and musical themes

The similarity of understanding is important for music experience and communication, but little is understood about the sources of this common knowledge. Although neural responses to the same piece of music are known to be similar across listeners, it remains unclear whether this neural response similarity is linked to musical understanding and the role of dynamic musical attributes in shaping it. Our study addresses this gap by investigating the relationship between neural response similarity, musical tension, and dynamic musical attributes. Using electroencephalography-based inter-subject correlation (EEG-ISC), we examined how the neural response similarity among listeners varies throughout the evaluation of musical tension in the first movement of Beethoven's Piano Sonata No. 8. Participants continuously rated the degree of alignment between musical events and their expectations, while neural activity was recorded using electroencephalography (EEG). The results showed that neural response similarity fluctuated in tandem with musical tension, with increased similarity observed during moments of heightened tension. This time-varying neural response similarity was influenced by two dynamic attributes contributing to musical tension: physical features and musical themes. Specifically, its fluctuation was driven by physical features, and the patterns of its variation were modulated by musical themes, with similar time-varying patterns observed across similar thematic materials. These findings offer valuable insight into the role of dynamic musical attributes in shaping neural response similarity, and reveal an important source and mechanism of shared musical understandings.

A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis

BACKGROUND

Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis.

METHODS

To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based).

RESULTS

Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580).

CONCLUSIONS

By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.

Effects of Phomopsidione on the Viability, Virulence, and Metabolites Profile of Methicillin-Resistant Staphylococcus aureus (MRSA)

Methicillin-resistant Staphylococcus aureus (MRSA) infections have become one of the most threatening multidrug-resistant pathogens. Thus, an ongoing search for anti-MRSA compounds remains an urgent need to effectively treating MRSA infections. Phomopsidione, a novel antibiotic isolated from Diaporthe fraxini, has previously demonstrated potent anti-candidal activity. The present study aimed to investigate the effects of phomopsidione on the viability, virulence, and metabolites profile of MRSA. MRSA was sensitive to phomopsidione in a concentration-dependent manner. Phomopsidione exhibited minimum inhibitory concentration and minimum bactericidal concentration of 62.5 and 500.00 µg/mL against MRSA on broth microdilution assay. The compound showed significant reduction in virulence factors production including extracellular polymeric substances quantification, catalase, and lipase. An untargeted metabolomics analysis using liquid chromatography-high resolution mass spectrometry revealed a significant difference in the metabolites profile of MRSA with 13 putatively identified discriminant metabolites. The present study suggested the potential of phomopsidione as a promising anti-MRSA agent.

Leveraging big data for causal understanding in mental health: a research framework

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Determinants of Dutch public health professionals' intention to use digital contact tracing support tools: A cross-sectional online questionnaire study

Contact tracing (CT) can be a resource intensive task for public health services. To alleviate their workload and potentially accelerate the CT-process, public health professionals (PHPs) may transfer some tasks in the identification, notification, and monitoring of contacts to cases and their contacts themselves, using 'digital contact tracing support tools' (DCTS-tools). In this study, we aimed to identify determinants of PHPs' intention to use DCTS-tools. Between February and April 2022, we performed a cross-sectional online questionnaire study among PHPs involved in CT for COVID-19 in the Netherlands. We built three random forest models to identify determinants of PHPs' intention to use DCTS-tools for the identification, notification, and monitoring of contacts, respectively. The online questionnaire was completed by 641 PHPs. Most respondents had a positive intention towards using DCTS-tools for the identification (64.5%), notification (58%), and monitoring (55.2%) of contacts. Random forest models were able to correctly predict the intention of 81%, 80%, and 81% of respondents to use DCTS-tools for the identification, notification, and monitoring of contacts, respectively. Top-determinants of having a positive intention are the anticipated effect of DCTS-tools on the feasibility and efficiency of CT (speed, workload, difficulty), the degree to which PHPs anticipated that cases and contacts may find it pleasant and may be willing to participate in CT using DCTS-tools, and the degree to which PHPs anticipated that cases and contacts are sufficiently supported in CT when using DCTS-tools. Most PHPs have a positive intention to involve cases and their contacts in the identification, notification, and monitoring stages of the CT-process through DCTS-tools. The identified top-determinants should be prioritized in the (future) development and implementation of DCTS-tools in public health practice. Citizens' perspectives on the use of DCTS-tools should be investigated in future research.

Proteomic profile of extracellular vesicles in maternal plasma of women with fetal death

OBJECTIVES

Fetal death is a complication of pregnancy caused by multiple etiologies rather than being the end-result of a single disease process. Many soluble analytes in the maternal circulation, such as hormones and cytokines, have been implicated in its pathophysiology. However, changes in the protein content of extracellular vesicles (EVs), which could provide additional insight into the disease pathways of this obstetrical syndrome, have not been examined. This study aimed to characterize the proteomic profile of EVs in the plasma of pregnant women who experienced fetal death and to evaluate whether such a profile reflected the pathophysiological mechanisms of this obstetrical complication. Moreover, the proteomic results were compared to and integrated with those obtained from the soluble fraction of maternal plasma.

METHODS

This retrospective case-control study included 47 women who experienced fetal death and 94 matched, healthy, pregnant controls. Proteomic analysis of 82 proteins in the EVs and the soluble fractions of maternal plasma samples was conducted by using a bead-based, multiplexed immunoassay platform. Quantile regression analysis and random forest models were implemented to assess differences in the concentration of proteins in the EV and soluble fractions and to evaluate their combined discriminatory power between clinical groups. Hierarchical cluster analysis was applied to identify subgroups of fetal death cases with similar proteomic profiles. A p-value of <.05 was used to infer significance, unless multiple testing was involved, with the false discovery rate controlled at the 10% level (q < 0.1). All statistical analyses were performed by using the R statistical language and environment-and specialized packages.

RESULTS

Nineteen proteins (placental growth factor, macrophage migration inhibitory factor, endoglin, regulated upon activation normal T cell expressed and presumably secreted (RANTES), interleukin (IL)-6, macrophage inflammatory protein 1-alpha, urokinase plasminogen activator surface receptor, tissue factor pathway inhibitor, IL-8, E-Selectin, vascular endothelial growth factor receptor 2, pentraxin 3, IL-16, galectin-1, monocyte chemotactic protein 1, disintegrin and metalloproteinase domain-containing protein 12, insulin-like growth factor-binding protein 1, matrix metalloproteinase-1(MMP1), and CD163) were found to have different plasma concentrations (of an EV or a soluble fraction) in women with fetal death compared to controls. There was a similar pattern of change for the dysregulated proteins in the EV and soluble fractions and a positive correlation between the log₂-fold changes of proteins significant in either the EV or the soluble fraction (ρ = 0.89, p < .001). The combination of EV and soluble fraction proteins resulted in a good discriminatory model (area under the ROC curve, 82%; sensitivity, 57.5% at a 10% false-positive rate). Unsupervised clustering based on the proteins differentially expressed in either the EV or the soluble fraction of patients with fetal death relative to controls revealed three major clusters of patients.

CONCLUSION

Pregnant women with fetal death have different concentrations of 19 proteins in the EV and soluble fractions compared to controls, and the direction of changes in concentration was similar between fractions. The combination of EV and soluble protein concentrations revealed three different clusters of fetal death cases with distinct clinical and placental histopathological characteristics.

A health data led approach for assessing potential health benefits of green and blue spaces: Lessons from an Irish case study

Research producing evidence-based information on the health benefits of green and blue spaces often has within its design, the potential for inherent or implicit bias which can unconsciously orient the outcomes of such studies towards preconceived hypothesis. Many studies are situated in proximity to specific or generic green and blue spaces (hence, constituting a green or blue space led approach), others are conducted due to availability of green and blue space data (hence, applying a green or blue space data led approach), while other studies are shaped by particular interests in the association of particular health conditions with presence of, or engagements with green or blue spaces (hence, adopting a health or health status led approach). In order to tackle this bias and develop a more objective research design for studying associations between human health outcomes and green and blue spaces, this paper discussed the features of a methodological framework suitable for that purpose after an initial, year-long, exploratory Irish study. The innovative approach explored by this study (i.e., the health-data led approach) first identifies sample sites with good and poor health outcomes from available health data (using data clustering techniques) before examining the potential role of the presence of, or engagement with green and blue spaces in creating such health outcomes. By doing so, we argue that some of the bias associated with the other three listed methods can be reduced and even eliminated. Finally, we infer that the principles and paradigm adopted by the health data led approach can be applicable and effective in analyzing other sustainability problems beyond associations between human health outcomes and green and blue spaces (e.g., health, energy, food, income, environment and climate inequality and justice etc.). The possibility of this is also discussed within this paper.

Trace element contamination in soils surrounding the open-cast coal mines of eastern Raniganj basin, India

Trace element pollution of soils surrounding coal-mining areas affects the health of local communities. The increasing coal-mining and associated activities in the Raniganj basin (east India) have led to increased soil concentration of certain trace elements. To quantify the elevated trace element (TE) concentrations in the soil surrounding coal-mining areas, 83 surface soil, coal, and shale samples were collected from open-cast mining areas of the eastern Raniganj basin. The soils present are sandy silt, silty sand, and silty in nature, but almost no clay. They are acidic (pH = 4.3) to slightly alkaline (pH = 7.9) with a mean electrical conductivity (EC) of 340.45 µS/cm and a mean total organic carbon (TOC) of 1.80%. The northern and western parts of the study area were found to be highly polluted by certain metallic trace elements. The relevant environmental indices, geoaccumulation index (Igeo), contamination factors (CF), enrichment factors (EF), and pollution load index (PLI) were calculated and assessed. Analysis revealed that Cr was highly enriched in these soil samples, followed by Pb, Co, Cu, Cd, Fe, Ni, Mn, Zn, As, and Al. Geostatistical analyses (correlation coefficients and principal component analysis) indicated that the occurrence of some trace elements (Al, Cd, Co, Cu, Fe, Mn, Ni, and Zn) is most likely linked to the various coal-mining operations in the study area. However, the anomalous Cr and Pb distributions are likely influenced by other anthropogenic, mainly industrial, inputs besides coal mining. These results justify the adoption of rigorous soil monitoring programs in the vicinity of coal-mining areas, to identify pollution hotspots and to develop strategies to reduce or mitigate such environmentally damaging pollution.

Connectomic insight into unique stroke patient recovery after rTMS treatment

An improved understanding of the neuroplastic potential of the brain has allowed advancements in neuromodulatory treatments for acute stroke patients. However, there remains a poor understanding of individual differences in treatment-induced recovery. Individualized information on connectivity disturbances may help predict differences in treatment response and recovery phenotypes. We studied the medical data of 22 ischemic stroke patients who received MRI scans and started repetitive transcranial magnetic stimulation (rTMS) treatment on the same day. The functional and motor outcomes were assessed at admission day, 1 day after treatment, 30 days after treatment, and 90 days after treatment using four validated standardized stroke outcome scales. Each patient underwent detailed baseline connectivity analyses to identify structural and functional connectivity disturbances. An unsupervised machine learning (ML) agglomerative hierarchical clustering method was utilized to group patients according to outcomes at four-time points to identify individual phenotypes in recovery trajectory. Differences in connectivity features were examined between individual clusters. Patients were a median age of 64, 50% female, and had a median hospital length of stay of 9.5 days. A significant improvement between all time points was demonstrated post treatment in three of four validated stroke scales utilized. ML-based analyses identified distinct clusters representing unique patient trajectories for each scale. Quantitative differences were found to exist in structural and functional connectivity analyses of the motor network and subcortical structures between individual clusters which could explain these unique trajectories on the Barthel Index (BI) scale but not on other stroke scales. This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.

Patient-Reported Outcomes Measurement Information System -29 Domains Interaction in Chronic Musculoskeletal Pain During Acupuncture: A Pilot Study

Objective

This pilot study explored interactions of domains of physical, psychologic, and social factors in the Patient-Reported Outcomes Measurement Information System® (PROMIS®)-29 system and their dynamic changes during acupuncture treatment of chronic musculoskeletal pain.

Materials and Methods

PROMIS-29 profile, version 2.1 was applied among participants with chronic musculoskeletal pain, who received acupuncture treatment for 5 weeks. Data from function-oriented and symptom-oriented domains as well as changes in pain intensity were evaluated at weeks 0, 3, and 5, in 9 patients who completed full sessions. Scores of the domains were analyzed by hierarchical cluster analysis at each timepoint to identify the patterns of interactions of PROMIS domains.

Results

Hierarchical cluster analysis revealed the existence of 2 main clusters: one consisting of pain, fatigue, and emotional domains; the other comprising physical function and social domains. The general pattern was stable but interactions were found throughout the treatment. The score for sleep disturbance did not improve but was correlated with different domains at varying stages of treatment.

Conclusions

Interaction between 2 clusters of pain with fatigue and emotional domains; and physical function with social domains showed that acupuncture produces holistic reductions in chronic musculoskeletal pain. However, the limitation of sample size and bias in this pilot study requires future research on the need to adopt an interdisciplinary and holistic approach to the recovery of patients with chronic musculoskeletal pain, who have dynamic needs.

Unsupervised clustering of venous thromboembolism patients by clinical features at presentation identifies novel endotypes that improve prognostic stratification

BACKGROUND

Individuals with acute venous thromboembolism (VTE) constitute a heterogeneous group of patients with diverse clinical characteristics and outcome.

OBJECTIVES

To identify endotypes of individuals with acute VTE based on clinical characteristics at presentation through unsupervised cluster analysis and to evaluate their molecular proteomic profile and clinical outcome.

METHODS

Data from 591 individuals from the Genotyping and Molecular phenotyping of Venous thromboembolism (GMP-VTE) project were explored. Hierarchical clustering was applied to 58 variables to define VTE endotypes. Clinical characteristics, three-year incidence of thromboembolic events or death, and acute-phase plasma proteomics were assessed.

RESULTS

Four endotypes were identified, exhibiting different patterns of clinical characteristics and clinical course. Endotype 1 (n = 300), comprising older individuals with comorbidities, had the highest incidence of thromboembolic events or death (HR [95 % CI]: 3.76 [1.96-7.19]), followed by endotype 4 (n = 127) (HR [95 % CI]: 2.55 [1.26-5.16]), characterised by men with history of VTE and provoking risk factors, and endotype 3 (n = 57) (HR [95 % CI]: 1.57 [0.63-3.87]), composed of young women with provoking risk factors, vs. reference endotype 2 (n = 107). The reference endotype was constituted by individuals diagnosed with PE without comorbidities, who had the lowest incidence of the investigated endpoint. Differentially expressed proteins associated with the endotypes were related to distinct biological processes, supporting differences in molecular pathophysiology. The endotypes had superior prognostic ability compared to existing risk stratifications such as provoked vs unprovoked VTE and D-dimer levels.

CONCLUSION

Four endotypes of VTE were identified by unsupervised phenotype-based clustering that diverge in clinical outcome and plasmatic protein signature. This approach might support the future development of individualized treatment in VTE.

Determinants of Vaccination and Willingness to Vaccinate against COVID-19 among Pregnant and Postpartum Women during the Third Wave of the Pandemic: A European Multinational Cross-Sectional Survey

With COVID-19 vaccination hesitancy at around 50% in the obstetric population, it is critical to identify which women should be addressed and how. Our study aimed to assess COVID-19 vaccination willingness among pregnant and postpartum women in Europe and to investigate associated determinants. This study was a cross-sectional, web-based survey conducted in Belgium, Norway, Switzerland, The Netherlands, and United Kingdom (UK) in June-August 2021. Among 3194 pregnant women, the proportions of women vaccinated or willing to be vaccinated ranged from 80.5% in Belgium to 21.5% in Norway. The associated characteristics were country of residence, chronic illness, history of flu vaccine, trimester of pregnancy, belief that COVID-19 is more severe during pregnancy, and belief that the COVID-19 vaccine is effective and safe during pregnancy. Among 1659 postpartum women, the proportions of women vaccinated or willing to be vaccinated ranged from 86.0% in the UK to 58.6% in Switzerland. The associated determinants were country of residence, chronic illness, history of flu vaccine, breastfeeding, and belief that the COVID-19 vaccine is safe during breastfeeding. Vaccine hesitancy in the obstetric population depends on medical history and especially on the opinion that the vaccine is safe and on the country of residence.

A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).

Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance

A comparison of neural network clustering (NNC) and hierarchical clustering (HC) is conducted to assess computing dominance of two machine learning (ML) methods for classifying a populous data of large number of variables into clusters. An accurate clustering disposition is imperative to investigate assembly-influence of predictors on a system over a course of time. Moreover, categorically designated representation of variables can assist in scaling down a wide data without loss of essential system knowledge. For NNC, a self-organizing map (SOM)-training was used on a local aqua system to learn distribution and topology of variables in an input space. Ternary features of SOM; sample hits, neighbouring weight distances and weight planes were investigated to institute an optical inference of system's structural attributes. For HC, constitutional partitioning of the data was executed through a coupled dissimilarity-linkage matrix operation. The validation of this approach was established through a higher value of cophenetic coefficient. Additionally, an HC-feature of stem-division was used to determine cluster boundaries. SOM visuals reported two locations' samples for remarkable concentration analogy and presence of 4 extremely out of range concentration parameter from among 16 samples. NNC analysis also demonstrated that singular conduct of 18 independent components over a period of time can be comparably inquired through aggregate influence of 6 clusters containing these components. However, a precise number of 7 clusters was retrieved through HC analysis for segmentation of the system. Composing elements of each cluster were also distinctly provided. It is concluded that simultaneous categorization of system's predictors (water components) and inputs (locations) through NNC and HC is valid to the precision probability of 0.8, as compared to data segmentation conducted with either of the methods exclusively. It is also established that cluster genesis through combined HC's linkage and dissimilarity algorithms and NNC is more reliable than individual optical assessment of NNC, where varying a map size in SOM will alter the association of inputs' weights to neurons, providing a new consolidation of clusters.

Multi-Morbidity and Risk of Breast Cancer among Women in the UK Biobank Cohort

(Multi-)Morbidity shares common biological mechanisms or risk factors with breast cancer. This study aimed to investigate the association between the number of morbidities and patterns of morbidity and the risk of female breast cancer. Among 239,436 women (40-69 years) enrolled in the UK Biobank cohort who had no cancer history at baseline, we identified 35 self-reported chronic diseases at baseline. We assigned individuals into morbidity patterns using agglomerative hierarchical clustering analysis. We fitted Cox models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer risk. In total, 58.4% of women had at least one morbidity, and the prevalence of multi-morbidity was 25.8%. During a median 7-year follow-up, there was no association between breast cancer risk (5326 cases) and either the number of morbidities or the identified clinically relevant morbidity patterns: no-predominant morbidity (reference), psychiatric morbidities (HR = 1.04, 95%CI 0.94-1.16), respiratory/immunological morbidities (HR = 0.98, 95%CI 0.90-1.07), cardiovascular/metabolic morbidities (HR = 0.93, 95%CI 0.81-1.06), and unspecific morbidities (HR = 0.98, 95%CI 0.89-1.07), overall. Among women younger than 50 years of age only, however, there was a significant association with psychiatric morbidity patterns compared to the no-predominant morbidity pattern (HR = 1.25, 95%CI 1.02-1.52). The other associations did not vary when stratifying by age at baseline and adherence to mammography recommendations. In conclusion, multi-morbidity was not a key factor to help identify patients at an increased risk of breast cancer.

Patient-Preference Diagnostics: Adapting Stated-Preference Methods to Inform Effective Shared Decision Making

BACKGROUND

While clinical practice guidelines underscore the need to incorporate patient preferences in clinical decision making, incorporating meaningful assessment of patient preferences in clinical encounters is challenging. Structured approaches that combine quantitative patient preferences and clinical evidence could facilitate effective patient-provider communication and more patient-centric health care decisions. Adaptive conjoint or stated-preference approaches can identify individual preference parameters, but they can require a relatively large number of choice questions or simplifying assumptions about the error with which preferences are elicited.

METHOD

We propose an approach to efficiently diagnose preferences of patients for outcomes of treatment alternatives by leveraging prior information on patient preferences to generate adaptive choice questions to identify a patient's proximity to known preference phenotypes. This information can be used for measuring sensitivity and specificity, much like any other diagnostic procedure. We simulated responses with varying levels of choice errors for hypothetical patients with specific preference profiles to measure sensitivity and specificity of a 2-question preference diagnostic.

RESULTS

We identified 4 classes representing distinct preference profiles for patients who participated in a previous first-time anterior shoulder dislocation (FTASD) survey. Posterior probabilities of class membership at the end of a 2-question sequence ranged from 87% to 89%. We found that specificity and sensitivity of the 2-question sequences were robust to respondent errors. The questions appeared to have better specificity than sensitivity.

CONCLUSIONS

Our results suggest that this approach could help diagnose patient preferences for treatments for a condition such as FTASD with acceptable precision using as few as 2 choice questions. Such preference-diagnostic tools could be used to improve and document alignment of treatment choices and patient preferences.

HIGHLIGHTS

Approaches that combine patient preferences and clinical evidence can facilitate effective patient-provider communication and more patient-centric healthcare decisions. However, diagnosing individual-level preferences is challenging, and no formal diagnostic tools exist.We propose a structured approach to efficiently diagnose patient preferences based on prior information on the distribution of patient preferences in a population.We generated a 2-question test of preferences for the outcomes associated with the treatment of first-time anterior shoulder dislocation.The diagnosis of preferences can help physicians discuss relevant aspects of the treatment options and proactively address patient concerns during the clinical encounter.

First report on quality and purity assessment of sweet almond oil in Brazilian body oils by gas chromatography and mass spectrometry

Sweet almond oil is a raw material with high-added value used in different products. Then, the aim of this study is to evaluate the quality and purity of 10 body oils based on sweet almond oils currently available in the Brazilian market. Fatty acid composition and triacylglycerol (TAG) profile were determined by gas chromatography with flame ionization detector (GC-FID) and atmospheric solids analysis probe mass spectrometry (ASAP-MS), respectively. The authenticity of samples was assessed using an analytical curve equation. Soybean oil was chosen as the adulterant because it is the cheapest vegetable oil commercialized in Brazil. Hierarchical clustering analysis (HCA) in conjunction with ASAP-MS classified product samples according to the type of vegetable oil (soybean and sweet almond oils). The addition of soybean oil (8.79% to 99.70%) was confirmed in samples. However, only two samples stated in their label the presence of soybean oil as an ingredient. These findings highlight the need for better oversight by regulatory bodies to ensure that consumers acquire high quality and authentic products based on equally high quality and purity of sweet almond oils.

Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI

BACKGROUND

Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive.

OBJECTIVES

Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures.

METHODS

A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes.

RESULTS

Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described.

CONCLUSION

Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

A Global Analysis of National Dietary Guidelines on Plant-Based Diets and Substitutions for Animal-Based Foods

Discussing plant-based diets and substitutions for animal-based foods in food-based dietary guidelines (FBDGs) can be a key step in making dietary recommendations more sustainable and healthy as well as more inclusive. The existing large-scale evaluations of FBDGs do not assess whether and to what extent countries cover the broad spectrum of plant-based diets and have policy positions on vegetarian diets, including vegan diets, and whether they mention specific plant-based alternatives to milk, dairy products, and meat. The main aim of this state-of-the-art review was to determine whether and how FBDGs provide such information. An overall 95 guidelines and 100 corresponding countries were assessed via an exploratory sequential mixed method. This involved qualitative explorative content analysis of the guidelines, followed by hierarchical cluster analysis. Furthermore, the Balanced Food Choice Index (BFCI) was constructed, which measures the extent to which FBDGs provide recommendations that cover the broad spectrum of plant-based diets, with some or no animal-based products. To explore the correlations between FBDGs' recommendations and ecological and economic country characteristics, ordinary least squares regression was used. It was found that most countries do not provide information to their citizens that cover the broad spectrum of plant-based diets, as indicated by the mean score of the BFCI (33.58 of 100 points). A total of 38 guidelines (40%) contain a position on vegetarian diets. Nearly half (45%) of all FBDGs already mention plant-based alternatives to meat or animal milk. The regressions showed that the BFCI correlates positively with countries' ecological efforts and negatively with the importance of animal-based products in their economies. This study demonstrates considerable information insufficiency in current FBDGs worldwide. FBDGs should provide recommendations for the broad spectrum of plant-based diets and balance the ethical, ecological, religious, and economic aspects that play a role in people's food choice.

Quantitative proteomics analysis to assess protein expression levels in the ovaries of pubescent goats

Background

Changes in the abundance of ovarian proteins play a key role in the regulation of reproduction. However, to date, no studies have investigated such changes in pubescent goats. Herein we applied isobaric tags for relative and absolute quantitation (iTRAQ) and liquid chromatography–tandem mass spectrometry to analyze the expression levels of ovarian proteins in pre-pubertal (n = 3) and pubertal (n = 3) goats.

Results

Overall, 7,550 proteins were recognized; 301 (176 up- and 125 downregulated) were identified as differentially abundant proteins (DAPs). Five DAPs were randomly selected for expression level validation by Western blotting; the results of Western blotting and iTRAQ analysis were consistent. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that DAPs were enriched in olfactory transduction, glutathione metabolism, and calcium signaling pathways. Besides, gene ontology functional enrichment analysis revealed that several DAPs enriched in biological processes were associated with cellular process, biological regulation, metabolic process, and response to stimulus. Protein–protein interaction network showed that proteins interacting with CDK1, HSPA1A, and UCK2 were the most abundant.

Conclusions

We identified 301 DAPs, which were enriched in olfactory transduction, glutathione metabolism, and calcium signaling pathways, suggesting the involvement of these processes in the onset of puberty. Further studies are warranted to more comprehensively explore the function of the identified DAPs and aforementioned signaling pathways to gain novel, deeper insights into the mechanisms underlying the onset of puberty.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08699-y.

Patterns of Screening Recall Behavior Among Subspecialty Breast Radiologists

RATIONALE AND OBJECTIVES

Determine whether there are patterns of lesion recall among breast imaging subspecialists interpreting screening mammography, and if so, whether recall patterns correlate to morphologies of screen-detected cancers.

MATERIALS AND METHODS

This Institutional Review Board-approved, retrospective review included all screening examinations January 3, 2012-October 1, 2018 interpreted by fifteen breast imaging subspecialists at a large academic medical center and two outpatient imaging centers. Natural language processing identified radiologist recalls by lesion type (mass, calcifications, asymmetry, architectural distortion); proportions of callbacks by lesion types were calculated per radiologist. Hierarchical cluster analysis grouped radiologists based on recall patterns. Groups were compared to overall practice and each other by proportions of lesion types recalled, and overall and lesion-specific positive predictive value-1 (PPV1).

RESULTS

Among 161,859 screening mammograms with 13,086 (8.1%) recalls, Hierarchical cluster analysis grouped 15 radiologists into five groups. There was substantial variation in proportions of lesions recalled: calcifications 13%-18% (Chi-square 45.69, p < 0.00001); mass 16%-44% (Chi-square 498.42, p < 0.00001); asymmetry 13%-47% (Chi-square 660.93, p < 0.00001) architectural distortion 6%-20% (Chi-square 283.81, p < 0.00001). Radiologist groups differed significantly in overall PPV1 (range 5.6%-8.8%; Chi-square 17.065, p = 0.0019). PPV1 by lesion type varied among groups: calcifications 9.2%-15.4% (Chi-square 2.56, p = 0.6339); mass 5.6%-8.5% (Chi-square 1.31, p = 0.8597); asymmetry 3.4%-5.9% (Chi-square 2.225, p = 0.6945); architectural distortion 5.6%-10.8% (Chi-square 5.810, p = 0.2138). Proportions of recalled lesions did not consistently correlate to proportions of screen-detected cancer.

CONCLUSION

Breast imaging subspecialists have patterns for screening mammography recalls, suggesting differential weighting of imaging findings for perceived malignant potential. Radiologist recall patterns are not always predictive of screen-detected cancers nor lesion-specific PPV1s.

Metabolomics and integrated network analysis reveal roles of endocannabinoids and large neutral amino acid balance in the ayahuasca experience

There has been a renewed interest in the potential use of psychedelics for the treatment of psychiatric conditions. Nevertheless, little is known about the mechanism of action and molecular pathways influenced by ayahuasca use in humans. Therefore, for the first time, our study aims to investigate the human metabolomics signature after consumption of a psychedelic, ayahuasca, and its connection with both the psychedelic-induced subjective effects and the plasma concentrations of ayahuasca alkaloids. Plasma samples of 23 individuals were collected both before and after ayahuasca consumption. Samples were analysed through targeted metabolomics and further integrated with subjective ratings of the ayahuasca experience (i.e., using the 5-Dimension Altered States of Consciousness Rating Scale [ASC]), and plasma ayahuasca-alkaloids using integrated network analysis. Metabolic pathways enrichment analysis using diffusion algorithms for specific KEGG modules was performed on the metabolic output. Compared to baseline, the consumption of ayahuasca increased N-acyl-ethanolamine endocannabinoids, decreased 2-acyl-glycerol endocannabinoids, and altered several large-neutral amino acids (LNAAs). Integrated network results indicated that most of the LNAAs were inversely associated with 9 out of the 11 subscales of the ASC, except for tryptophan which was positively associated. Several endocannabinoids and hexosylceramides were directly associated with the ayahuasca alkaloids. Enrichment analysis confirmed dysregulation in several pathways involved in neurotransmission such as serotonin and dopamine synthesis. In conclusion, a crosstalk between the circulating LNAAs and the subjective effects is suggested, which is independent of the alkaloid concentrations and provides insights into the specific metabolic fingerprint and mechanism of action underlying ayahuasca experiences.

Determinants of resistant starch accumulation in wheat endosperm

A part of the big three cereal crops in the world, wheat has become a major constituent of the everyday food chain and is grown at a massive scale to meet global demands. This makes it an important crop from an economic as well as food security perspective. Selection of high-quality cultivars and consistent trait enhancement for such cultivars is crucial, and in light of new challenges from climate change, this has become an absolute necessity of time. In this regard, we conducted a detailed qualitative and quantitative trait analysis for multiple commercially viable varieties of wheat, and corresponding results were subjected to a series of critical statistical analyses. Final results have shown that five cultivars including Uqaab-2000, Faisalabad- 85, Anmol-19, NARC-2009, and Pirsabak-2004 depicts higher levels of various essential qualitative and quantitative traits (including Starch content, grain weight, RS content, Protein content, etc.) and are most viable varieties for further growth and trait enhancements to meet regional and global food challenges.

Forecast of Medical Costs in Health Companies Using Models Based on Advanced Analytics

Forecasting medical costs is crucial for planning, budgeting, and efficient decision making in the health industry. This paper introduces a proposal to forecast costs through techniques such as a standard model of long short-term memory (LSTM); and patient grouping through k-means clustering in the Keralty group, one of Colombia’s leading healthcare companies. It is important to highlight its implications for the prediction of cost time series in the health sector from a retrospective analysis of the information of services invoiced to health companies. It starts with the selection of sociodemographic variables related to the patient, such as age, gender and marital status, and it is complemented with health variables such as patient comorbidities (cohorts) and induced variables, such as service provision frequency and time elapsed since the last consultation (hereafter referred to as “recency”). Our results suggest that greater accuracy can be achieved by first clustering and then using LSTM networks. This implies that a correct segmentation of the population according to the usage of services represented in costs must be performed beforehand. Through the analysis, a cost projection from 1 to 3 months can be conducted, allowing a comparison with historical data. The reliability of the model is validated by different metrics such as RMSE and Adjusted R2. Overall, this study is intended to be useful for healthcare managers in developing a strategy for medical cost forecasting. We conclude that the use of analytical tools allows the organization to make informed decisions and to develop strategies for optimizing resources with the identified population.

Metabolomic Characterisation of Discriminatory Metabolites Involved in Halo Blight Disease in Oat Cultivars Caused by Pseudomonas syringae pv. coronafaciens

The metabolome is the underlying biochemical layer of the phenotype and offers a functional readout of the cellular mechanisms involved in a biological system. Since metabolites are considered end-products of regulatory processes at a cellular level, their levels are considered the definitive response of the biological system to genetic or environmental variations. The metabolome thus serves as a metabolic fingerprint of the biochemical events that occur in a biological system under specific conditions. In this study, an untargeted metabolomics approach was applied to elucidate biochemical processes implicated in oat plant responses to Pseudomonas syringae pv. coronafaciens (Ps-c) infection, and to identify signatory markers related to defence responses and disease resistance against halo blight. Metabolic changes in two oat cultivars (“Dunnart” and “SWK001”) responding to Ps-c, were examined at the three-leaf growth stage and metabolome changes monitored over a four-day post-inoculation period. Hydromethanolic extracts were analysed using an ultra-high-performance liquid chromatography (UHPLC) system coupled to a high-definition mass spectrometer (MS) analytical platform. The acquired multi-dimensional data were processed using multivariate statistical analysis and chemometric modelling. The validated chemometric models indicated time- and cultivar-related metabolic changes, defining the host response to the bacterial inoculation. Further multivariate analyses of the data were performed to profile differential signatory markers, putatively associated with the type of launched defence response. These included amino acids, phenolics, phenolic amides, fatty acids, flavonoids, alkaloids, terpenoids, lipids, saponins and plant hormones. Based on the results, metabolic alterations involved in oat defence responses to Ps-c were elucidated and key signatory metabolic markers defining the defence metabolome were identified. The study thus contributes toward a more holistic understanding of the oat metabolism under biotic stress.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

Lung histopathologic clusters in severe COVID-19: a link between clinical picture and tissue damage

BACKGROUND

Autoptic pulmonary findings have been described in severe COVID-19 patients, but evidence regarding the correlation between clinical picture and lung histopathologic patterns is still weak.

METHODS

This was a retrospective cohort observational study conducted at the referral center for infectious diseases in northern Italy. Full lung autoptic findings and clinical data of patients who died from COVID-19 were analyzed. Lung histopathologic patterns were scored according to the extent of tissue damage. To consider coexisting histopathologic patterns, hierarchical clustering of histopathologic findings was applied.

RESULTS

Whole pulmonary examination was available in 75 out of 92 full autopsies. Forty-eight hospitalized patients (64%), 44 from ICU and four from the medical ward, had complete clinical data. The histopathologic patterns had a time-dependent distribution with considerable overlap among patterns. Duration of positive-pressure ventilation (p < 0.0001), mean positive end-expiratory pressure (PEEP) (p = 0.007), worst serum albumin (p = 0.017), interleukin 6 (p = 0.047), and kidney SOFA (p = 0.001) differed among histopathologic clusters. The amount of PEEP for long-lasting ventilatory treatment was associated with the cluster showing the largest areas of early and late proliferative diffuse alveolar damage. No pharmacologic interventions or comorbidities affected the lung histopathology.

CONCLUSIONS

Our study draws a comprehensive link between the clinical and pulmonary histopathologic findings in a large cohort of COVID-19 patients. These results highlight that the positive end-expiratory pressures and the duration of the ventilatory treatment correlate with lung histopathologic patterns, providing new clues to the knowledge of the pathophysiology of severe SARS-CoV-2 pneumonia.

Infection with a hypervirulent strain of Helicobacter pylori primes gastric cells toward intestinal transdifferentiation

BACKGROUND

Intestinal metaplasia, gastric-to-intestinal transdifferentiation, occurs as a result of the misexpression of certain regulatory factors, leading to genetic reprogramming. Here, we have evaluated the H. pylori-induced expression patterns of these candidate genes.

METHODS

The expression levels of 1) tissue-specific transcription factors (RUNX3, KLF5, SOX2, SALL4, CDX1 and CDX2), 2) stemness factors (TNFRSF19, LGR5, VIL1) and 3) tissue-specific mucins (MUC5AC, MUC2) were evaluated by quantitative real-time PCR in gastric primary cells (GPCs), in parallel with two gastric cancer (MKN45 and AGS) cell lines, up to 96h following H. pylori infection.

RESULTS

Following H. pylori infection of GPCs, RUNX3 declined at 24h post infection (-6.2 ± 0.3) and remained downregulated for up to 96h. Subsequently, overexpression of self-renewal and pluripotency transcription factors, KLF5 (3.6 ± 0.2), SOX2 (7.6 ± 0.5) and SALL4 (4.3 ± 0.2) occurred. The expression of TNFRSF19 and LGR5, demonstrated opposing trends, with an early rise of the former (4.5 ± 0.3) at 8h, and a simultaneous fall of the latter (-1.8 ± 0.5). This trend was reversed at 96h, with the decline in TNFRSF19 (-5.5 ± 0.2), and escalation of LGR5 (2.6 ± 0.2) and VIL1 (1.8 ± 0.3). Ultimately, CDX1 and CDX2 were upregulated by 1.9 and 4.7-fold, respectively. The above scenario was, variably observed in MKN45 and AGS cells.

CONCLUSION

Our data suggests an interdependent gene regulatory network, induced by H. pylori infection. This interaction begins with the downregulation of RUNX3, upregulation of self-renewal and pluripotency transcription factors, KLF5, SOX2 and SALL4, leading to the downregulation of TNFRSF19, upregulation of LGR5 and aberrant expression of intestine-specific transcription factors, potentially facilitating the process of gastric-to-intestinal transdifferentiation.

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real‐time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye‐view of their characteristics, functions, and applications. The review also presents the state‐of‐the‐art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI‐assisted support systems relating to clinical decisions are presented and discussed.

The Relationship between Negative Symptoms and Both Emotion Management and Non-social Cognition in Schizophrenia Spectrum Disorders

OBJECTIVE

There is ongoing debate regarding the relationship between clinical symptoms and cognition in schizophrenia spectrum disorders (SSD). The present study aimed to explore the potential relationships between symptoms, with an emphasis on negative symptoms, and social and non-social cognition.

METHOD

Hierarchical cluster analysis with k-means optimisation was conducted to characterise clinical subgroups using the Scale for the Assessment of Negative Symptoms and Scale for the Assessment of Positive Symptoms in n = 130 SSD participants. Emergent clusters were compared on the MATRICS Consensus Cognitive Battery, which measures non-social cognition and emotion management as well as demographic and clinical variables. Spearman's correlations were then used to investigate potential relationships between specific negative symptoms and emotion management and non-social cognition.

RESULTS

Four distinct clinical subgroups were identified: 1. high hallucinations, 2. mixed symptoms, 3. high negative symptoms, and 4. relatively asymptomatic. The high negative symptom subgroup was found to have significantly poorer emotion management than the high hallucination and relatively asymptomatic subgroups. No further differences between subgroups were observed. Correlation analyses revealed avolition-apathy and anhedonia-asociality were negatively correlated with emotion management, but not non-social cognition. Affective flattening and alogia were not associated with either emotion management or non-social cognition.

CONCLUSIONS

The present study identified associations between negative symptoms and emotion management within social cognition, but no domains of non-social cognition. This relationship may be specific to motivation, anhedonia and apathy, but not expressive deficits. This suggests that targeted interventions for social cognition may also result in parallel improvement in some specific negative symptoms.

Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study

Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.

Longitudinal analysis of symptom-based clustering in patients with primary Sjogren's syndrome: a prospective cohort study with a 5-year follow-up period

BACKGROUND

Sjogren's syndrome (SS) is a heterogenous disease with various phenotypes. We aimed to provide a relevant subclassification based on symptom-based clustering for patients with primary (p) SS.

METHODS

Data from patients in a prospective pSS cohort in Korea were analysed. Latent class analysis (LCA) was performed using patient reported outcomes, including pain, fatigue, dryness, and anxiety/depression. Clinical and laboratory differences between the classes were analysed. Latent transition analysis (LTA) was applied to the longitudinal data (annually for up to 5 years) to assess temporal stability of the classifications.

RESULTS

LCA identified three classes among 341 patients with pSS (i.e., 'high symptom burden', 'dryness dominant', 'low symptom burden'). Each group had distinct laboratory and clinical phenotypes. LTA revealed that class membership remained stable over time. Baseline class predicted future salivary gland function and damage accrual represented by a Sjogren's syndrome disease damage index.

CONCLUSION

Symptom-based clustering of heterogenous patients with primary Sjogren's syndrome provided a relevant classification supported by temporal stability over time and distinct phenotypes between the classes. This clustering strategy may provide more homogenous groups of pSS patients for novel treatment development and predict future phenotypic evolvement.

The molecular profile of mucosal melanoma

Herein, we wanted to explore the molecular landscape of mucosal melanoma from different sites and identify potential molecular targets for future therapy. Mucosal melanomas (N = 40) from different sites (conjunctiva, sinonasal cavity, rectum, and vagina) were investigated. Targeted next-generation sequencing along with Nanostring gene expression profiling was performed. Genetically, conjunctival melanoma was characterized by BRAF-V600E (30%) and NF1 mutations (17%). Mucosal melanomas at nonsun-exposed sites harbored alterations in NRAS, KIT, NF1, along with atypical BRAF mutations. When comparing the gene expression profile of conjunctival melanoma and nonsun-exposed mucosal melanoma, 41 genes were found to be significantly deregulated. Programmed death-ligand 1 (PD-L1) presented a significant sixfold upregulation in conjunctival melanoma compared to the other mucosal melanomas. While melanomas of the sinonasal cavity, vagina, and rectum are molecularly similar, conjunctival melanoma is characterized by a higher frequency of BRAF-V600E mutations and differential expression of several genes involved in the immune response.

Multimorbidity clusters among long-term breast cancer survivors in Spain: Results of the SURBCAN study

The disease management of long-term breast cancer survivors (BCS) is hampered by the scarce knowledge of multimorbidity patterns. The aim of our study was to identify multimorbidity clusters among long-term BCS and assess their impact on mortality and health services use. We conducted a retrospective study using electronic health records of 6512 BCS from Spain surviving at least 5 years. Hierarchical cluster analysis was used to identify groups of similar patients based on their chronic diagnoses, which were assessed using the Clinical Classifications Software. As a result, multimorbidity clusters were obtained, clinically defined and named according to the comorbidities with higher observed/expected prevalence ratios. Multivariable Cox and negative binomial regression models were fitted to estimate overall mortality risk and probability of contacting health services according to the clusters identified. 83.7% of BCS presented multimorbidity, essential hypertension (34.5%) and obesity and other metabolic disorders (27.4%) being the most prevalent chronic diseases at the beginning of follow-up. Five multimorbidity clusters were identified: C1-unspecific (29.9%), C2-metabolic and neurodegenerative (28.3%), C3-anxiety and fractures (9.7%), C4-musculoskeletal and cardiovascular (9.6%) and C5-thyroid disorders (5.3%). All clusters except C5-thyroid disorders were associated with higher mortality compared to BCS without comorbidities. The risk of mortality in C4 was increased by 64% (adjusted hazard ratio 1.64, 95% confidence interval 1.52-2.07). Stratified analysis showed an increased risk of death among BCS with 5 to 10 years of survival in all clusters. These results help to identify subgroups of long-term BCS with specific needs and mortality risks and to guide BCS clinical practice regarding multimorbidity.

The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.

Understanding the Role and Value of Process Quality Indicators in Older Vascular Surgery Inpatients

BACKGROUND

Despite the development of geriatrics surgery process quality indicators (QIs), few studies have reported on these QIs in routine surgical practice. Even less is known about the links between these QIs and clinical outcomes, and patient characteristics. We aimed to measure geriatrics surgery process QIs, and investigate the association between process QIs and outcomes, and QIs and patient characteristics, in hospitalized older vascular surgery patients.

METHODS

This was a prospective cohort study of 150 consecutive patients aged ≥ 65 years admitted to a tertiary vascular surgery unit. Occurrence of geriatrics surgery process QIs as part of routine vascular surgery care was measured. Associations between QIs and high-risk patient characteristics, and QIs and clinical outcomes were assessed using clustered heatmaps.

RESULTS

QI occurrence rate varied substantially from 2% to 93%. Some QIs, such as cognition and delirium screening, documented treatment preferences, and geriatrician consultation were infrequent and clustered with high-risk patient characteristcs. There were two major process-outcome clusters: (a) multidisciplinary consultations, communication and screening-based process QIs with multiple adverse outcomes, and (b) documentation and prescribing-related QIs with fewer adverse outcomes.

CONCLUSIONS

Clustering patterns of process QIs with clinical outcomes are complex, and there is a differential occurrence of QIs by patient characteristics. Prospective intervention studies that report on implemented QIs, outcomes and patient characteristics are needed to better understand the causal pathways between process QIs and outcomes, and to help prioritize targets for quality improvement in the care of older surgical patients.

Spectrum-effect relationship between UPLC fingerprints and anti-lung cancer effect of Panax ginseng

OBJECTIVES

Lung cancer has the highest mortality rate among the various types of cancer. Panax ginseng (C. A. Mey). is a popular anti-cancer herbal supplement. The quality control of ginseng is crucial to ensure its clinical efficacy. This study aimed to establish new quality control methods for ginseng and to identify its main active components responsible for lung cancer treatment.

METHODS

Ultra-high-performance liquid chromatography (UPLC) was used to establish fingerprints of 18 batches of ginseng. CCK-8 test was performed to evaluate the inhibitory activity of ginseng on Lewis lung cancer (LLC) cells. The spectrum-effect relationship analysis of ginseng was assessed by canonical correlation analysis (CCA) and bioactivity validation.

KEY FINDINGS

Six common peaks were identified and the variation coefficients were determined. The 18 batches of ginseng inhibited the proliferation of LLC cells to different degrees, showing different half maximal inhibitory concentration (IC₅₀ ) values. Spectrum-effect relationship analysis showed that ginsenoside Ro is the main anti-proliferative constituent of LLC cell.

CONCLUSIONS

Spectrum-effect relationship is suitable for quality control of ginseng used for lung cancer. It is also effective in discovering the active ingredients related to the clinical efficacy of traditional Chinese medicine.

Hierarchical cluster analysis in the study of the effect of cytokine expression patterns on endometrial repair and receptivity after hysteroscopic adhesiolysis

Background

In a previous study, we reported that amnion promotes endometrial cell growth by regulating cytokines. In this study, hierarchical cluster analysis enabled the evaluation of cytokine expression changes after amnion treatment to be explored by cluster patterns. The role of IL1B on endometrial repair and receptivity was revealed.

Methods

A total of 30 patients were recruited in this clinical trial (NCT02496052) of hysteroscopic adhesiolysis. They were randomly allocated into an amnion grafts group (amnion group) and a control group. After hysteroscopic adhesiolysis, a Foley catheter covered with a sterilized freeze-dried amnion graft was inserted into the uterine cavity of the participants in the amnion group, whereas for the control group, a Foley catheter without amnion graft was inserted. After surgery, patient follow-up was done for a year. Uterine exudates were collected every day for seven days after surgery, and analyzed by enzyme-linked immunosorbent assays. Hierarchical cluster analysis was performed to compare expression patterns of each cytokine. Single-gene gene set enrichment analysis and differentially expressed genes enrichment analysis of IL1B were performed using NCBI GEO (N=151) to evaluate its potential mechanisms and impact on endometrial receptivity.

Results

Compared to the control group, cytokine expression patterns of the amnion group revealed significant stratifications, which were highly correlated with the expression levels of IL1B on the sixth to seventh day after surgery, improving the pregnant rate. Wilcoxon test revealed significantly low expression levels of IL1B in the reduced endometrial receptivity group compared to the normal group. Moreover, gene set enrichment analysis showed that lysosomes, cell cycle, and calcium signaling pathways were associated with the biological processes in which IL1B plays a role. Screening and enrichment analyses of differentially expressed genes further verified the mechanisms of action of IL1B on endometrial repair and receptivity recovery.

Conclusions

Amnion promotes endometrial repair and receptivity by altering the expression levels and patterns of IL1B. Furthermore, by affecting lysosomal, cell cycle, and calcium signaling pathways, IL1B may be one of the factors involved in endometrial repair and receptivity recovery.

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.

Distribution of the T12 erector spinal muscle plane block in the dorsal region guided by ultrasound

Background

This study aimed to explore the distribution of the erector spinal muscle plane block of the thoracic 12 vertebral body (T12) in the dorsal region guided by ultrasound.

Methods

A total of 28 patients, who underwent elective lumbar surgery, were enrolled in the present study. These patients were aged between 18 and 65 years, and the American Society of Anesthesiologists (ASA) grade was 1 or 2. The block of the T12 transverse process erector spinal muscle was performed under the guidance of ultrasound, and each side was injected with 25 ml of 0.4% ropivacaine hydrochloride + 2 mg of dexamethasone. The back areas were measured using the cold-warm method (the back area was divided into 11 areas [T7–S1] with the body surface marker). At 10, 20, 30, 40, 50, and 60 min after the drug injection, the effectiveness of the regional block was recorded. The presence of puncture hematoma, local anesthesia drug poisoning, nausea, vomiting, headache, and dizziness after the block was recorded.

Results

The range of the T12 transverse process block was basically fixed at 30 min after the single injection. No pneumothorax, hematoma, or local anesthetic poisoning occurred in any of the patients.

Conclusion

The effective longitudinal plane of the T12 transverse process erector spinal muscle block was mainly distributed in the T9–L5 dorsal cutaneous branches, and the distribution of the block area was safe and stable.