Is Antiviral Treatment with Remdesivir at the Acute Phase of SARS-CoV-2 Infection Effective for Decreasing the Risk of Long-Lasting Post-COVID Symptoms?

The aim of this study was to investigate the effects of administrating Remdesivir at the acute COVID-19 phase on developing post-COVID symptoms in previously hospitalized COVID-19 survivors by controlling factors such as age, sex, body mass index, and vaccination status. A case-control study was performed. Hospitalized COVID-19 survivors who had received intravenous Remdesivir during the acute phase (n = 216) were matched by age, sex, body mass index, and vaccination status with survivors who did not receive antiviral treatment (n = 216). Participants were asked to self-report the presence of any post-COVID symptom (defined as a symptom that started no later than three months after infection) and whether the symptom persisted at the time of study (mean: 18.4, SD: 0.8 months). Anxiety levels (HADS-A), depressive symptoms (HADS-D), sleep quality (PSQI), and severity/disability (FIC) were also compared. The multivariate analysis revealed that administration of Remdesivir at the acute COVID-19 phase was a protective factor for long-term COVID development (OR0.401, 95%CI 0.256-0.628) and specifically for the following post-COVID symptoms: fatigue (OR0.399, 95%CI 0.270-0.590), pain (OR0.368, 95% CI 0.248-0.548), dyspnea at rest (OR0.580, 95%CI 0.361-0.933), concentration loss (OR0.368, 95%CI 0.151-0.901), memory loss (OR0.399, 95%CI 0.270-0.590), hair loss (OR0.103, 95%CI 0.052-0.207), and skin rashes (OR0.037, 95%CI 0.005-0.278). This study supports the potential protective role of intravenous administration of Remdesivir during the COVID-19 acute phase for long-lasting post-COVID symptoms in previously hospitalized COVID-19 survivors.

Presence of SARS-CoV-2 RNA in COVID-19 survivors with post-COVID symptoms 2 years after hospitalization: The VIPER study.

The SARS-CoV-2 VIrus PERsistence (VIPER) study investigated the presence of long-lasting SARS-CoV-2 RNA in plasma, stool, urine, and nasopharyngeal samples in COVID-19 survivors. The presence of SARS-CoV-2 RNA reverse transcription polymerase chain reactions (RT-PCR) were analyzed within plasma, stool, urine, and nasopharyngeal swab samples in COVID-19 survivors with post-COVID symptoms and a comparison group of COVID-19 survivors without post-COVID symptoms matched by age, sex, body mass index and vaccination status. Participants self-reported the presence of any post-COVID symptom (defined as a symptom that started no later than 3 months after the initial infection). Fifty-seven (57.9% women, age: 51.1, standard deviation [SD]: 10.4 years) previously hospitalized COVID-19 survivors with post-COVID symptoms and 55 (56.4% women, age: 50.0, SD: 12.8 years) matched individuals who had a past SARS-CoV-2 infection without post-COVID symptoms were evaluated 27 (SD 7.5) and 26 (SD 8.7) months after hospital discharge, respectively. The presence of SARS-CoV-2 RNA was identified in three nasopharyngeal samples of patients with post-COVID symptoms (5.2%) but not in plasma, stool, or urine samples. Thus, SARS-CoV-2 RNA was not identified in any sample of survivors without post-COVID symptoms. The most prevalent post-COVID symptoms consisted of fatigue (93%), dyspnea, and pain (both, 87.7%). This study did not find SARS-CoV-2 RNA in plasma, stool, or urine samples, 2 years after the infection. A prevalence of 5.2% of SARS-CoV-2 RNA in nasopharyngeal samples, suggesting a potential active or recent reinfection, was found in patients with post-COVID symptoms. These results do not support the association between SARS-CoV-2 RNA in plasma, stool, urine, or nasopharyngeal swab samples and post-COVID symptomatology in the recruited population.

Investigating the fluctuating nature of post-COVID pain symptoms in previously hospitalized COVID-19 survivors: the LONG-COVID-EXP multicenter study.

Objective: This cohort study used Sankey plots and exponential bar plots for visualizing the fluctuating nature and trajectory of post-COVID pain in previously hospitalized COVID-19 survivors. Methods: A cohort of 1266 subjects hospitalised because of COVID-19 during the first wave of the pandemic were scheduled for a telephone interview at 8.4 (T1), 13.2 (T2), and 18.3 (T3) months in average after hospitalization for collecting data about post-COVID pain. Patients were asked for about pain symptomatology that was attributed to the infection. Hospitalization and clinical data were collected from medical records. Results: The prevalence of myalgia as COVID-19-associated symptom was 29.82% (n = 389) at hospitalization (T0). The prevalence of post-COVID pain was 41.07% (n = 520) at T1, 34.29% (n = 434) at T2, and 28.47% (n = 360) at T3. The recovery exponential curve revealed a decrease trend visualizing that post-COVID pain improved over the time span investigated. Pain in the lower extremity and widespread pain were the most prevalent locations. Female sex (OR 1.507, 95% CI 1.047-2.169), pre-existing pain symptoms (OR 1.724, 95% CI 1.237-2.403), headache as onset-symptom (OR 2.374, 95% CI 1.550-3.639), days at hospital (OR 1.012, 95% CI 1.000-1.025), and presence of post-COVID pain at T1 (OR 13.243, 95% CI 9.428-18.601) were associated with post-COVID pain at T2. Only the presence of post-COVID pain at T1 (OR 5.383, 95% CI 3.896-7.439) was associated with post-COVID pain at T3. Conclusion: Current results show a fluctuating evolution with a decreasing tendency of post-COVID pain during the first years after hospitalization. The development of post-COVID pain soon after SARS-CoV-2 infection predispose for long-lasting chronic pain.

Trajectory of anxiety/depressive symptoms and sleep quality in individuals who had been hospitalized by COVID-19: The LONG-COVID-EXP multicenter study.

OBJECTIVE: To apply Sankey plots and exponential bar plots for visualizing the evolution of anxiety/depressive symptoms and poor sleep in previously hospitalized COVID-19 survivors. METHODS: A sample of 1266 subjects who were hospitalized due to a SARS-CoV-2 from March-May 2020 were assessed at 8.4 (T1), 13.2 (T2) and 18.3 (T3) months after hospitalization. The Hospital Anxiety and Depression Scale was used to determine anxiety (HADS-A) and depressive (HADS-D) symptoms. The Pittsburgh Sleep Quality Index (PSQI) evaluated sleep quality. Clinical features, onset symptoms and hospital data were collected from medical records. RESULTS: Sankey plots revealed that the prevalence of anxiety symptomatology (HADS-A ≥ 8 points) was 22.5% (n = 285) at T1, 17.6% (n = 223) at T2, and 7.9% (n = 100) at T3, whereas the prevalence of depressive symptoms (HADS-D ≥ 8 points) was 14.6% (n = 185) at T1, 10.9% (n = 138) at T2, and 6.1% (n = 78) at T3. Finally, the prevalence of poor sleep (PSQI≥8 points) decreased from 32.8% (n = 415) at T1, to 28.8% (n = 365) at T2, and to 24.8% (n = 314) at T3. The recovery curves show a decrease trend visualizing that these symptoms recovered the following years after discharge. The regression models did not reveal medical records associated with anxiety/depressive symptoms or poor sleep. CONCLUSION: The use of Sankey plots shows a fluctuating evolution of anxiety/depressive symptoms and poor sleep during the first years after the infection. In addition, exponential bar plots revealed a decrease prevalence of these symptoms during the first years after hospital discharge. No risk factors were identified in this cohort.

Inflammatory Polymorphisms (IL-6 rs1800796, IL-10 rs1800896, TNF-α rs1800629, and IFITM3 rs12252) Are Not Associated with Post-COVID Symptoms in Previously Hospitalized COVID-19 Survivors.

The aim of this study was to identify the association between four selected inflammatory polymorphisms with the development of long-term post-COVID symptoms in subjects who had been hospitalized due to SARS-CoV-2 infection during the first wave of the pandemic. These polymorphisms were selected as they are associated with severe COVID-19 disease and cytokine storm, so they could be important to prognoses post-COVID. A total of 408 (48.5% female, age: 58.5 ± 14.0 years) previously hospitalized COVID-19 survivors participated. The three potential genotypes of the following four single-nucleotide polymorphisms, IL-6 rs1800796, IL-10 rs1800896, TNF-α rs1800629, and IFITM3 rs12252, were obtained from non-stimulated saliva samples of the participants. The participants were asked to self-report the presence of any post-COVID symptoms (defined as symptoms that had started no later than one month after SARS-CoV-2 acute infection) and whether the symptoms persisted at the time of the study. At the time of the study (mean: 15.6, SD: 5.6 months after discharge), 89.4% of patients reported at least one post-COVID symptom (mean number of symptoms: 3.0; SD: 1.7). Fatigue (69.3%), pain (40.9%), and memory loss (27.2%) were the most prevalent post-COVID symptoms in the total sample. Overall, no differences in the post-COVID symptoms depending on the IL-6 rs1800796, IL-10 rs1800896, TNF-α rs1800629, and IFITM3 rs12252 genotypes were seen. The four SNPs assessed, albeit having been previously associated with inflammation and COVID-19 severity, did not cause a predisposition to the development of post-COVID symptoms in the previously hospitalized COVID-19 survivors.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Neuropathic post-COVID pain symptomatology is not associated with serological biomarkers at hospital admission and hospitalization treatment in COVID-19 survivors.

Objective: Evidence suggests that individuals who had survived to coronavirus disease, 2019 (COVID-19) could develop neuropathic post-COVID pain. This study investigated the association of serological biomarkers and treatments received during hospitalization with development of neuropathic-associated symptoms. Methods: One hundred and eighty-three (n = 183) previously hospitalized COVID-19 survivors during the first wave of the pandemic were assessed in a face-to-face interview 9.4 months after hospitalization. Nineteen serological biomarkers, hospitalization data, and treatment during hospitalization were obtained from medical records. Neuropathic pain symptoms (Self-Report Leeds Assessment of Neuropathic Scale), sleep quality (Pittsburgh Sleep Quality Index), pain catastrophizing (Pain Catastrophizing Scale) and anxiety/depressive levels (Hospital Anxiety and Depression Scale) were assessed. Results: The prevalence of post-COVID pain was 40.9% (n = 75). Fifteen (20%) patients reported neuropathic symptoms. Overall, no differences in hospitalization data and serological biomarkers were identified according to the presence or not of neuropathic-associated symptoms. Patients with post-COVID pain had the highest neutrophil count, and post hoc analysis revealed that patients with neuropathic post-COVID associated symptoms had lower neutrophil count (p = 0.04) compared with those without neuropathic pain, but differences were small and possible not clinically relevant. No differences in fatigue, dyspnea, brain fog, anxiety or depressive levels, poor sleep, or pain catastrophism between patients with and without neuropathic symptoms were found. Conclusion: It seems that neuropathic-like post-COVID pain symptoms are not associated with neither of assessed serological biomarkers at hospital admission nor hospitalization treatments received in this cohort of hospitalized COVID-19 survivors.

Trajectory of post-COVID brain fog, memory loss, and concentration loss in previously hospitalized COVID-19 survivors: the LONG-COVID-EXP multicenter study.

Objective: This study aimed to apply Sankey plots and exponential bar plots for visualizing the trajectory of post-COVID brain fog, memory loss, and concentration loss in a cohort of previously hospitalized COVID-19 survivors. Methods: A sample of 1,266 previously hospitalized patients due to COVID-19 during the first wave of the pandemic were assessed at 8.4 (T1), 13.2 (T2), and 18.3 (T3) months after hospital discharge. They were asked about the presence of the following self-reported cognitive symptoms: brain fog (defined as self-perception of sluggish or fuzzy thinking), memory loss (defined as self-perception of unusual forgetfulness), and concentration loss (defined as self-perception of not being able to maintain attention). We asked about symptoms that individuals had not experienced previously, and they attributed them to the acute infection. Clinical and hospitalization data were collected from hospital medical records. Results: The Sankey plots revealed that the prevalence of post-COVID brain fog was 8.37% (n = 106) at T1, 4.7% (n = 60) at T2, and 5.1% (n = 65) at T3, whereas the prevalence of post-COVID memory loss was 14.9% (n = 189) at T1, 11.4% (n = 145) at T2, and 12.12% (n = 154) at T3. Finally, the prevalence of post-COVID concentration loss decreased from 6.86% (n = 87) at T1, to 4.78% (n = 60) at T2, and to 2.63% (n = 33) at T3. The recovery exponential curves show a decreasing trend, indicating that these post-COVID cognitive symptoms recovered in the following years after discharge. The regression models did not reveal any medical record data associated with post-COVID brain fog, memory loss, or concentration loss in the long term. Conclusion: The use of Sankey plots shows a fluctuating evolution of post-COVID brain fog, memory loss, or concentration loss during the first years after the infection. In addition, exponential bar plots revealed a decrease in the prevalence of these symptoms during the first years after hospital discharge. No risk factors were identified in this cohort.

Serological Biomarkers at Hospital Admission and Hospitalization Treatments Are Not Related to Sensitization-Associated Symptoms in Patients with Post-COVID Pain.

Current evidence suggests that a group of patients who had survived coronavirus disease, 2019 (COVID-19) and developed post-COVID pain can exhibit altered nociceptive processing. The role of serological biomarkers and hospitalization treatments in post-COVID pain is unclear. This study aimed to investigate the association of serological biomarkers and treatments received during hospitalization with sensitization-associated symptoms in COVID-19 survivors with post-COVID pain. One hundred and eighty-three (n = 183) patients who had been hospitalized due to COVID-19 in one urban hospital of Madrid (Spain) during the first wave of the pandemic were assessed in a face-to-face interview 9.4 (SD 3.4) months after hospitalization. Levels of 19 serological biomarkers, hospitalization data, and treatments during hospitalization were obtained from hospital records. Sensitization-associated symptoms (Central Sensitization Inventory, CSI), sleep quality (Pittsburgh Sleep Quality Index, PSQI), pain catastrophism (Pain Catastrophizing Scale), and anxiety/depressive level (Hospital Anxiety and Depression Scale, HADS) were assessed. The prevalence of post-COVID pain was 40.9% (n = 75). Twenty-nine (38.6%) patients had sensitization-associated symptoms. Overall, no differences in hospitalization data and serological biomarkers were identified according to the presence of sensitization-associated symptoms. The analysis revealed that patients with sensitization-associated symptoms exhibited higher lymphocyte count and lower urea levels than those without sensitization-associated symptoms, but differences were small. Pain catastrophism and depressive levels, but not fatigue, dyspnea, brain fog, anxiety levels, or poor sleep, were higher in individuals with sensitization-associated symptoms. In conclusion, this study revealed that sensitization-associated post-COVID pain symptoms are not associated with serological biomarkers at hospital admission and hospitalization treatments received.

Trajectory of Post-COVID Self-Reported Fatigue and Dyspnoea in Individuals Who Had Been Hospitalized by COVID-19: The LONG-COVID-EXP Multicenter Study.

Fatigue and dyspnoea are common post-COVID symptoms. The aim of this study was to apply Sankey plots and exponential bar plots for visualizing the evolution and trajectory of post-COVID fatigue and dyspnoea symptoms in a cohort of previously hospitalized COVID-19 survivors. A total of 1266 previously hospitalized patients due to COVID-19 participated in this multicentre study. They were assessed at hospital admission (T0), 8.4 months (T1), 13.2 months (T2) and 18.3 months (T3) after hospital discharge and were asked about the presence of self-reported fatigue or dyspnoea symptoms. Fatigue was defined as a self-perceived feeling of constant tiredness and/or weakness whereas dyspnoea was defined as a self-perceived feeling of shortness of breath at rest. We specifically asked for fatigue and dyspnoea that participants attributed to the infection. Clinical/hospitalization data were collected from hospital medical records. The prevalence of post-COVID fatigue was 56.94% (n = 721) at T1, 52.31% (n = 662) at T2 and 42.66% (n = 540) at T3. The prevalence of dyspnoea at rest decreased from 28.71% (n = 363) at hospital admission (T0), to 21.29% (n = 270) at T1, to 13.96% (n = 177) at T2 and 12.04% (n = 153) at T3. The Sankey plots revealed that 469 (37.08%) and 153 (12.04%) patients exhibited fatigue and dyspnoea at all follow-up periods. The recovery exponential curves show a decreased prevalence trend, showing that fatigue and dyspnoea recover the following three years after hospitalization. The regression models revealed that the female sex and experiencing the symptoms (e.g., fatigue, dyspnoea) at T1 were factors associated with the presence of post-COVID fatigue or dyspnoea at T2 and T3. The use of Sankey plots shows a fluctuating evolution of post-COVID fatigue and dyspnoea during the first two years after infection. In addition, exponential bar plots revealed a decreased prevalence of these symptoms during the first years after. The female sex is a risk factor for the development of post-COVID fatigue and dyspnoea.

Apolipoprotein E (ApoE) ε4 Genotype (ApoE rs429358-ApoE rs7412 Polymorphisms) Is Not Associated with Long COVID Symptoms in Previously Hospitalized COVID-19 Survivors.

The role of genetics as a predisposing factor related to an increased risk of developing long COVID symptomatology is under debate. The aim of the current secondary analysis was to identify the association between the Apolipoprotein E (ApoE) gene, a gene affecting cholesterol metabolism and previously associated with a higher risk of SARS-CoV-2 infection and COVID-19 severity, and the development of long COVID in a cohort of individuals who had been hospitalized by SARS-CoV-2 infection. Unstimulated whole saliva samples were collected from 287 previously hospitalized COVID-19 survivors. Three genotypes of the ApoE gene (ApoE ε2, ε3, ε4) were obtained based on the combination of ApoE rs429358 and ApoE rs7412 polymorphisms. Participants were asked to self-report the presence of any post-COVID symptom in a face-to-face interview at 17.8 ± 5.2 months after hospital discharge and medical records were obtained. Each participant reported 3.0 (1.9) post-COVID symptoms. Overall, no significant differences in long COVID symptoms were observed depending on the ApoE genotype (ApoE ε2, ApoE ε3, ApoE ε4). The presence of the ApoE ε4 genotype, albeit associated with a higher risk of SARS-CoV-2 infection and COVID-19 severity, did not appear to predispose for the presence of long COVID in our cohort of previously hospitalized COVID-19 survivors.

Prevalence of Self-Reported Anosmia and Ageusia in Elderly Patients Who Had Been Previously Hospitalized by SARS-CoV-2: The LONG-COVID-EXP Multicenter Study.

We explored two different graph methods for visualizing the prevalence of self-reported post-COVID anosmia and ageusia in a large sample of individuals who had been previously hospitalized in five different hospitals. A cohort of 1266 previously hospitalized COVID-19 survivors participated. Participants were assessed at hospitalization (T0) and at three different follow-up periods: 8.4 (T1), 13.2 (T2), and 18.3 (T3) months after hospital discharge. They were asked about the presence of self-reported anosmia and ageusia that they attributed to infection. Anosmia was defined as a self-perceived feeling of complete loss of smell. Ageusia was defined as a self-perceived feeling of complete loss of taste. Data about hospitalization were recorded from medical records. The results revealed that the prevalence of anosmia decreased from 8.29% (n = 105) at hospitalization (T0), to 4.47% (n = 56) at T1, to 3.27% (n = 41) at T2, and 3.35% (n = 42) at T3. Similarly, the prevalence of ageusia was 7.10% (n = 89) at the onset of SARS-CoV-2 infection (T0), but decreased to 3.03% (n = 38) at T1, to 1.99% (n = 25) at T2, and 1.36% (n = 17) at T3. The Sankey plots showed that only 10 (0.8%) and 11 (0.88%) patients exhibited anosmia and ageusia throughout all the follow-ups. The exponential curves revealed a progressive decrease in prevalence, demonstrating that self-reported anosmia and ageusia improved in the years following hospitalization. The female sex (OR4.254, 95% CI 1.184-15.294) and sufferers of asthma (OR7.086, 95% CI 1.359-36.936) were factors associated with the development of anosmia at T2, whereas internal care unit admission was a protective factor (OR0.891, 95% CI 0.819-0.970) for developing anosmia at T2. The use of a graphical method, such as a Sankey plot, shows that post-COVID self-reported anosmia and ageusia exhibit fluctuations during the first years after SARS-CoV-2 infection. Additionally, self-reported anosmia and ageusia also show a decrease in prevalence during the first years after infection, as expressed by exponential bar plots. The female sex was associated with the development of post-COVID anosmia, but not ageusia, in our cohort of elderly patients previously hospitalized due to COVID-19.

Trajectory of Gastrointestinal Symptoms in Previously Hospitalized COVID-19 Survivors: The Long COVID Experience Multicenter Study.

This multicenter cohort study used Sankey plots and exponential bar plots to visualize the fluctuating evolution and the trajectory of gastrointestinal symptoms in previously hospitalized COVID-19 survivors during the first 18 months after acute SARS-CoV-2 infection. A total of 1266 previously hospitalized COVID-19 survivors were assessed at four points: hospital admission (T0), at 8.4 months (T1), at 13.2 months (T2), and at 18.3 months (T3) after hospitalization. Participants were asked about their overall gastrointestinal symptoms and particularly diarrhea. Clinical and hospitalization data were collected from hospital medical records. The prevalence of overall gastrointestinal post-COVID symptomatology was 6.3% (n = 80) at T1, 3.99% (n = 50) at T2 and 2.39% (n = 32) at T3. The prevalence of diarrhea decreased from 10.69% (n = 135) at hospital admission (T0), to 2.55% (n = 32) at T1, to 1.04% (n = 14) at T2, and to 0.64% (n = 8) at T3. The Sankey plots revealed that just 20 (1.59%) and 4 (0.32%) patients exhibited overall gastrointestinal post-COVID symptoms or diarrhea, respectively, throughout the whole follow-up period. The recovery fitted exponential curves revealed a decreasing prevalence trend, showing that diarrhea and gastrointestinal symptoms recover during the first two or three years after COVID-19 in previously hospitalized COVID-19 survivors. The regression models did not reveal any symptoms to be associated with the presence of gastrointestinal post-COVID symptomatology or post-COVID diarrhea at hospital admission or at T1. The use of Sankey plots revealed the fluctuating evolution of gastrointestinal post-COVID symptoms during the first two years after infection. In addition, exponential bar plots revealed the decreased prevalence of gastrointestinal post-COVID symptomatology during the first three years after infection.

Clostridioides difficile: Characterization of the circulating toxinotypes in an Argentinean public hospital / Clostridioides difficile: caracterización de los toxinotipos circulantes en un hospital público de Argentina

Abstract Clostridioides difficile is a spore-forming anaerobe microorganism associated to nosocomial diarrhea. Its virulence is mainly associated with TcdA and TcdB toxins, encoded by their respective tcdA and tcdB genes. These genes are part of the pathogenicity locus (PaLoc). Our aim was to characterize relevant C. difficile toxinotypes circulating in the hospital setting. The tcdA and tcdB genes were amplified and digested with different restriction enzymes: EcoRI for tcdA; HincII and AccI for tcdB. In addition, the presence of the cdtB (binary toxin) gene, TcdA and TcdB toxins by dot blot and the cytotoxic effect of culture supernatants on Vero cells, were evaluated. Altogether, these studies revealed three different circulating toxinotypes according to Rupnik's classification: 0, I and VIII, being the latter the most prevalent one. Even though more studies are certainly necessary (e.g. sequencing analysis), it is worth noting that the occurrence of toxinotype I could be related to the introduction of bacteria from different geographical origins. The multivariate analysis conducted on the laboratory values of individuals infected with the most prevalent toxinotype (VIII) showed that the isolates associated with fatal outcomes (GCD13, GCD14 and GCD22) are located in regions of the biplots related to altered laboratory values at admission. In other patients, although laboratory values at admission were not correlated, levels of urea, creatinine and white blood cells were positively correlated after the infection was diagnosed. Our study reveals the circulation of different toxinotypes of C. difficile strains in this public hospital. The variety of toxinotypes can arise from pre-existing microorganisms as well as through the introduction of bacteria from other geographical regions. The existence of microorganisms with different pathogenic potential is relevant for the control, follow-up, and treatment of the infections.

Resumen Clostridioides difficile es un anaerobio esporulado que se asocia con episodios de diarreas hospitalarias. Su virulencia se encuentra vinculada, principalmente, a las toxinas TcdA y TcdB, codificadas por sus respectivos genes, tcdA y tcdB, que son parte de un locus de patogenicidad (PaLoc). Nuestro objetivo fue caracterizar los toxinotipos de C. difficile circulantes en un hospital público. Los genes tcdA y tcdB fueron amplificados y digeridos con diferentes enzimas de restricción: EcoRI para tcdA; HincII y AccI para tcdB. Además, se evaluó la presencia de cdtB (gen de la toxina binaria B) y de las toxinas A y B (por dot blot), así como el efecto citotóxico de sobrenadantes de cultivo sobre células Vero. En conjunto, estos estudios revelaron tres toxinotipos circulantes según la clasificación de Rupnik: 0, I y VIII; el más prevalente fue el último. Aunque son necesarios más estudios (ej., secuenciación), es interesante notar que la presencia del toxinotipo I podría estar relacionada con la introducción de bacterias de diferente origen geográfico. En los pacientes infectados con el toxinotipo VIII, el análisis multivariante de los resultados de laboratorio mostró que los aislamientos asociados a decesos (GCD13, GCD14 y GCD22) estaban situados en regiones de los biplots relacionados con valores de laboratorio alterados al momento de la internación. En los otros pacientes, aunque no se observó correlación entre los valores de laboratorio al momento de la internación y la evolución clínica, los niveles de urea, creatinina y recuento de glóbulos blancos estuvieron correlacionados positivamente entre sí una vez diagnosticada la infección. Nuestro estudio revela la circulación de diferentes toxinotipos de C. difficile en un mismo hospital público. La variedad de toxinotipos puede originarse a partir de microorganismos preexistentes en la región, así como también por la introducción de bacterias provenientes de otras regiones geográficas. La existencia de microorganismos con diferente potencial patogénico es relevante para el control, el seguimiento y el tratamiento de las infecciones.

Clustering analysis reveals different profiles associating long-term post-COVID symptoms, COVID-19 symptoms at hospital admission and previous medical co-morbidities in previously hospitalized COVID-19 survivors.

PURPOSE: To identify subgroups of COVID-19 survivors exhibiting long-term post-COVID symptoms according to clinical/hospitalization data by using cluster analysis in order to foresee the illness progress and facilitate subsequent prognosis. METHODS: Age, gender, height, weight, pre-existing medical comorbidities, Internal Care Unit (ICU) admission, days at hospital, and presence of COVID-19 symptoms at hospital admission were collected from hospital records in a sample of patients recovered from COVID-19 at five hospitals in Madrid (Spain). A predefined list of post-COVID symptoms was systematically assessed a mean of 8.4 months (SD 15.5) after hospital discharge. Anxiety/depressive levels and sleep quality were assessed with the Hospital Anxiety and Depression Scale and Pittsburgh Sleep Quality Index, respectively. Cluster analysis was used to identify groupings of COVID-19 patients without introducing any previous assumptions, yielding three different clusters associating post-COVID symptoms with acute COVID-19 symptoms at hospital admission. RESULTS: Cluster 2 grouped subjects with lower prevalence of medical co-morbidities, lower number of COVID-19 symptoms at hospital admission, lower number of post-COVID symptoms, and almost no limitations with daily living activities when compared to the others. In contrast, individuals in cluster 0 and 1 exhibited higher number of pre-existing medical co-morbidities, higher number of COVID-19 symptoms at hospital admission, higher number of long-term post-COVID symptoms (particularly fatigue, dyspnea and pain), more limitations on daily living activities, higher anxiety and depressive levels, and worse sleep quality than those in cluster 2. CONCLUSIONS: The identified subgrouping may reflect different mechanisms which should be considered in therapeutic interventions.

Clostridioides difficile: Characterization of the circulating toxinotypes in an Argentinean public hospital.

Clostridioides difficile is a spore-forming anaerobe microorganism associated to nosocomial diarrhea. Its virulence is mainly associated with TcdA and TcdB toxins, encoded by their respective tcdA and tcdB genes. These genes are part of the pathogenicity locus (PaLoc). Our aim was to characterize relevant C. difficile toxinotypes circulating in the hospital setting. The tcdA and tcdB genes were amplified and digested with different restriction enzymes: EcoRI for tcdA; HincII and AccI for tcdB. In addition, the presence of the cdtB (binary toxin) gene, TcdA and TcdB toxins by dot blot and the cytotoxic effect of culture supernatants on Vero cells, were evaluated. Altogether, these studies revealed three different circulating toxinotypes according to Rupnik's classification: 0, I and VIII, being the latter the most prevalent one. Even though more studies are certainly necessary (e.g. sequencing analysis), it is worth noting that the occurrence of toxinotype I could be related to the introduction of bacteria from different geographical origins. The multivariate analysis conducted on the laboratory values of individuals infected with the most prevalent toxinotype (VIII) showed that the isolates associated with fatal outcomes (GCD13, GCD14 and GCD22) are located in regions of the biplots related to altered laboratory values at admission. In other patients, although laboratory values at admission were not correlated, levels of urea, creatinine and white blood cells were positively correlated after the infection was diagnosed. Our study reveals the circulation of different toxinotypes of C. difficile strains in this public hospital. The variety of toxinotypes can arise from pre-existing microorganisms as well as through the introduction of bacteria from other geographical regions. The existence of microorganisms with different pathogenic potential is relevant for the control, follow-up, and treatment of the infections.

Clustering analysis identifies two subgroups of women with fibromyalgia with different psychological, cognitive, health-related, and physical features but similar widespread pressure pain sensitivity.

OBJECTIVE: Given that identification of groups of patients can help to better understand risk factors related to each group and to improve personalized therapeutic strategies, this study aimed to identify subgroups (clusters) of women with fibromyalgia syndrome (FMS) according to pain, pain-related disability, neurophysiological, cognitive, health, psychological, or physical features. METHODS: Demographic, pain, sensory, pain-related disability, psychological, health, cognitive, and physical variables were collected in 113 women with FMS. Widespread pressure pain thresholds were also assessed. K-means clustering was used to identify groups of women without any previous assumption. RESULTS: Two clusters exhibiting similar widespread sensitivity to pressure pain (pressure pain thresholds) but differing in the remaining variables were identified. Overall, women in one cluster exhibited higher pain intensity and pain-related disability; more sensitization-associated and neuropathic pain symptoms; higher kinesiophobia, hypervigilance, and catastrophism levels; worse sleep quality; higher anxiety/depressive levels; lower health-related function; and worse physical function than women in the other cluster. CONCLUSIONS: Cluster analysis identified one group of women with FMS exhibiting worse sensory, psychological, cognitive, and health-related features. Widespread sensitivity to pressure pain seems to be a common feature of FMS. The present results suggest that this group of women with FMS might need to be treated differently.