Anaemia, anisocytosis, malnutrition  are common health problems in developing countries with children being the most vulnerable. These conditions have negative impacts on human performance, growth and development, and can further be complicated if comorbidity exists within a holoendemic stratum with strong and perennial malaria parasite transmission such as the Mount Cameroon area. The study aimed at determining the prevalence and severity malnutrition, anaemia and anisocytosis in children ≤ 5 years, living in the conflict hit malaria perennial transmission zone of the Mount Cameroon area.A cross-sectional community-based survey involving 628 children ≤ 5 years was conducted. Malaria parasitaemia was confirmed by Giemsa-stained microscopy and the density was log transformed. Haemoglobin (Hb), mean cell volume and red blood cell distribution width were estimated using an auto-haematology analyser and defined according to WHO standards. Anthropometric indices were analysed and compared with WHO growth reference standards using WHO Anthro software.Plasmodium infection, anaemia, microcytic anaemia, anisocytosis and stunting were prevalent in 36.0, 72.8, 30.1, 54.1 and 29.0% of the children, respectively. The ≤ 24 months children were more moderately stunted (14.7%), with higher prevalence of microcytic anaemia (38.8%) and anisocytosis (68.8%)  when compared with the older children. The mean Hb level in the study population was 10.04 g/dL with children ≤ 24 months having the least mean haemoglobin level (9.69 g/dL) when compared with their older counterparts at P < 0.001. The odds of having anisocytosis were highest among children who were malnourished , those infected with malaria parasites , and whose parents had a primary  and secondary levels of education .Malaria, anaemia, anisocytosis and undernutrition still remain severe public health concerns among children ≤ 60 months in the Mount Cameroon area. This therefore emphasizes the need for the implementation of consistent policies, programmes and activities to avoid malaria, anaemia, anisocytosis and stunting in the paediatric age group. The World Health Organization (WHO) African Region continues to bear the greatest burden of malaria, with children under the age of five bearing the greatest toll. Globally, the region accounted for 95% of all malaria cases (228 million) and 96% of all malaria deaths (602 000) in 2020, with Cameroon accounting for 2.9% and 2.4% of these numbers, respectively. BetweenPlasmodium falciparum causing the most severe anaemia, and a significant risk of death [The aetiology of anaemia is often complex, and in African children, its epidemiology overlaps with a combination of nutritional deficiencies, infectious diseases  , 5, helmof death . Anaemiaof death –14 howevof death . It is wof death , and deaof death  and has Indicators of blood such as haemoglobin level, mean cell corpuscular volume (MCV) and red blood cell distribution width (RDW) are used to analyse haematological changes. Low haemoglobin level reflects the severity of anaemia, meanwhile MCV and RDW are sensitive and specific indices to identify iron deficiency anaemia –20. Low Malnutrition is a complicated condition with a multiple aetiology and a wide range of clinical manifestations. Acute malnutrition is characterized by wasting (low weight for height), while chronic malnutrition is characterized by stunting (low height for age). In 2020, nearly 45% of deaths among children under the age of five were due to malnutrition with low and middle-income countries being the most affected . Still iThe study was carried out in 3 rural communities  located in the Mount Cameroon area, Fako Division of the South West Region of Cameroon. These communities have been effectively described by Sumbele et al. . The MouThe study population included pre-school children of both sexes aged ≤ 5 years whose parent/caregiver consented through signing an informed consent form. This study included children who weighed more than 5 kg and excluded children with severe malaria, children who had blood transfused two months prior to the commencement of the study or had other diseases requiring immediate hospitalization.The study design was cross-sectional and it was carried out in the three communities in Mount Cameroon area between April and May 2018. Each community was divided into blocks. Within each block, all the households with children ≤ 5 years of age were selected. In a household where only one child within that age was present, the child was selected automatically. In cases where more than one child within the age group was present in a household, only one was randomly selected. Dates for sample collection was scheduled for each selected family. Each head/leader of a block (quarter head) served as a relay agent to remind potential participants of their scheduled dates of sample collection. Participants were invited to the community’s sample collection location by the local chief and coordinated by head of the block.Study methodology and benefits were explained to the parents and legal guardians. The study team proceeded with the collection of samples at specified identified collection sites after obtaining informed consent from the parents/care givers.n = Z2pq/d2; Where z = 1.96: confidence level test statistics at the desired level of significance; p = 32.6%, prevalence of malnutrition in children ≤ 5 years in the study area [q = 1-p: proportion of healthy children and d = acceptable error willing to be committed (0.05). The minimum estimated sample size calculated was approximately 345. Assuming 15% non-response rate, the minimum sample size obtained was 397. To obtain a more insightful information, lesser margin of error, higher confidence level and models with more accuracy, the final sample size was increased to 628 individuals.The sample size was calculated using the following formula : n = Z2pudy area ; q = 1-pResponses on (i) demographics ; (ii) socioeconomic status-related variables ; (iii) malaria knowledge level ; (iv) Fever management methods; (v) malaria preventive practices, such as bed net ownership, physical integrity, number, and use); (vi) and indoor residual spray (IRS) experience were collected using a structured questionnaire adopted from Asoba et al. (2019) . The useo C or higher. A measuring tape and a Terraillon weighing scale  were used to measure height and weight respectively. The tape was fastened to a locally produced woodwork that functioned as a stadiometer to ensure the precision. Malnutrition indices such as height-for-age (HA), weight-for-age (WA), and weight-for-height (WH) standard deviation (SD) scores (Z scores) were calculated using the WHO AnthroPlus for personal computers manual and the WHO growth reference curves [A digital thermometer was used to take the axillary temperature, and fever was defined as a temperature of 37.5e curves . If a chAbout 3–6 µL of whole blood were dispensed directly on the same slide for the preparation of thick and thin blood films, respectively, from the 4 mL of blood taken by venepuncture. The films were stained with 10% Giemsa and examined in the laboratory according to conventional methods . CountinBriefly, the blood samples were placed on a multi-mixer rotator for uniform mixing. A complete blood count was run following the manufacturer’s instructions using an auto-haematology analyser (MINRAY 2800 BC) to obtain haemoglobin concentration (Hb), haematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular haemoglobin concentration (MCHC), mean corpuscular haemoglobin (MCH), and red blood cell distribution width coefficient of variation (RDW-CV).Anisocytosis (RDW > 15%) .Anaemia (Hb < 11 g/dL) and further classified as mild , moderate  and severe (Hb < 7 g/dL).Microcytosis (MCV < 67 fL) for children < 2 years and MCV < 73 fL for children 2–5 years.Microcytic anaemia (Hb < 11.0 g/dL + MCV < 67 fL or Hb < 11.0 g/dL + MCV < 73 fL) depending on the age [WHO  classifiU test and the Kruskal–Wallis test. Associations between predictor variables and anisocytosis were assessed using both bivariate and multivariate logistic regression analysis. Odd ratios (ORs) and 95% confidence intervals (CIs) were computed. Any covariate with a P value < 0.2 in the bivariate analysis was subsequently included in the final multivariable logistic model. Significant levels were measured at 95% confidence interval (CI) with significant differences designated at P < 0.05.Data were entered into logbooks and then transferred to Microsoft Excel spreadsheets. The Statistical Package for Social Sciences (SPSS) version 23  software was used to analyse the data after it was cleaned. The descriptive statistics were evaluated using percentages and haemoglobin levels were summarized into means and standard deviations (SD). The Chi-square test (χ2) was used to examine the comparison between malaria parasite, anaemia, anisocytosis and malnutrition  as dependent variables with demographic and clinical characteristics as independent variables. Prior to analysis, the malaria parasite densities were log transformed. To assess the intensity of infection in the study population, the geometric mean parasite densities (GMPDs) were employed, and differences were analysed using the Mann–Whitney Following an ethical clearance from the Institutional Review Board hosted by the Faculty of Health Sciences, University of Buea (2017/004/UB/FHS/IRB), the study was accepted with an administrative authorization from the South West Regional Delegation of Public Health, Cameroon. At presentation, informed consent/assent forms were given or read and explained to parents or caregivers of the children. The information page and consent/assent forms both clearly indicated the study's goal and advantages, as well as the amount of blood to be obtained from each child. The study included only children who gave verbal assent in addition to their parents written informed consent. Participation was entirely optional, and parents or caregivers had the right to withdraw their child from the study at any time. All the information acquired was kept in strict confidence. Although the data was coded, the identity of the sample that was analysed was not revealed. Malaria cases, as well as those with moderate-to-severe anaemia and malnutrition, were all referred to the nearest health facility for treatment and follow-up.P. falciparum infection in the study area was 36.0% (226/628). Out of the 226 children positive for malaria, 33.3% were males and 38.4% females, with the ≤ 24 months age group having the highest prevalence of malaria when compared with the other age groups , while those whose parents had a tertiary level of education had the least prevalence  at P < 0.001. In addition, the prevalence of P. falciparum infection was lower in children who owned a bed net  when compared with those who did not  at P < 0.001. Moreover, children who used bed nets  had a lower prevalence of malaria parasitaemia compared with those who didn’t  at P < 0.001. However, the overall prevalence of P. falciparum infection was similar among children with malnutrition, wasting, underweight and stunting when compared to those who did not. In contrast, prevalence of P. falciparum infection was significantly higher among children with anaemia  and anisocytosis  compared with those without anaemia (P < 0.001) and anisocytosis (P = 0.003).A total of 628 preschool children of both sexes were included in the study. The overall prevalence of ps Table . ChildreThe overall prevalence of malnutrition, stunting, wasting and underweight in the study population was 40.9% (95% CI = 37.1–44.8), 29.0% (95% CI = 25.6–32.6), 18.2% (95% CI = 15.3–21.4), and 21.2% (95% CI = 18.2–24.5), respectively , underweight  and stunted  than their older counterparts at P = 0.004, P < 0.001, P < 0.001 and P < 0.001, respectively.As seen in Fig. P < 0.001, as shown in Table P = 0.014.The mean Hb level in the study population was 10.04 g/dL with children ≤ 24 months having the least mean haemoglobin level  when compared with their older counterparts at P < 0.002 and P < 0.001, respectively.Anaemia, microcytic anaemia, non-microcytic anaemia and anisocytosis was prevalent in 72.8% (95% CI = 69.2–76.1), 30.1% (95% CI = 26.6–33.8), 42.7% (95% CI = 38.9–46.7) and 54.1% (95% CI = 50.2–58.0) of the study population, respectively. Sex did not significantly affect the prevalence of anaemia, microcytic anaemia and anisocytosis as shown in Table P < 0.001 , wasted (78.9%), underweight (76.7%), stunted (65.9%) when compared with their well-nourished counterparts at 01 Table .P = 0.005), had malaria parasite (P = 0.007), and whose parents had a primary (P = 0.002) and secondary levels of education (P = 0.017) were more likely to have anisocytosis. Children from parents with primary and secondary level of education were 3.51 and 2.69 times at higher odds of having anisocytosis than children from parents with no formal level of education, as shown in Table The logistic regression model with anisocytosis as dependent variable and sex, age, family size, educational level of parents/caregivers, fever, malnutrition, stunting, wasting, underweight, malaria, as well as malnutrition demonstrated that children who were malnourished P = 0.00, had malChildhood is a time when the major developmental milestones are more nutritionally demanding and could be compromised by malaria, anaemia, anisocytosis and malnutrition which are serious public health concerns, particularly in low- and middle-income nations such as Cameroon.The overall malaria parasitaemia of 36.0% observed by microscopy in the study population confirms earlier studies by Asoba et al.  that mal. [According to the findings, children who owned and used their ITNs had a considerably decreased prevalence of malaria parasitaemia. This is consistent with the findings of Yekabong et al.  in Camer. .In line with the findings of Ebai et al.  in otherWhen compared with their peers, malaria prevalence was greater in anaemic (40.0%) children and those with anisocytosis 41.2%). In addition, findings from the study indicated malaria positive children were 1.87-fold more likely have anisocytosis, when compared with their negative counterparts. In previous investigations, the link between malaria parasitaemia and anaemia has been clearly established , 11. Mal.2%. In aThe burden of malnutrition (40.9%), stunting (29.0%), wasting (18.2%), and underweight (21.2%) in the population represents serious public health concern in the Mount Cameroon area. These observations matched those of Manjong et al.  who repoThe burden of malnutrition, stunting, wasting and underweight in the ≤ 24 months were comparable to that of their older counterparts. However, the ≤ 24 months were more moderately wasted (17.4%), underweight (18.8%) and stunted (14.7%) than their older age counterparts. Previous research has linked the onset of growth stalling with an increased chance of death by the age of 24 months . Also, aMalaria and undernutrition morbidity are frequently linked, and children who are seriously afflicted by one are frequently afflicted by the other. Malnourished, stunted, wasting, and underweight children had considerably greater malaria parasitaemia. This is in line with a study from Ethiopia that reported malaria as a risk factor for malnutrition . UndernuThe prevalence of anaemia (72.8%), microcytic anaemia (30.1%), and anisocytosis (54.1%), among the children in this study area, is still unacceptably high. This study's anaemic prevalence is similar to the 77.3% found by Asoba et al.  among 5-Anthropometric indices were used to determine nutritional status, which may have underestimated the magnitude when compared to biochemical data. It would have been beneficial to add the dietary diversity score and household food insecurity status, both of which have a causal relationship with children's nutritional status. Nevertheless, the high prevalence of undernutrition among the children in this study warrants immediate intervention.Malaria, anaemia, anisocytosis and undernutrition are still public health concerns among children ≤ 5 years in the Mount Cameroon area. Evidence on the cumulative negative impact of these comorbidities especially stunting is seen over a child's lifetime. It highlights the need for a more integrated approach for preventative and treatment techniques to stop this process. This therefore emphasizes the importance of consistent policy and the implementation of programmes and activities to avoid malaria, anaemia, anisocytosis and undernutrition particularly stunting in children under 60 years.


Autonomous sensory meridian response (ASMR) is a warm tingling sensation which is often accompanied by feelings of calmness and relaxation. The present study examined the effects of an ASMR video on mood, attention, heart rate (HR), electrodermal activity (EDA), electroencephalography (EEG) and the interaction with personality factors in 38 young adults . Based on the ASMR-checklist responses of having tingles during watching the ASMR video 15 participants out of 38 were classified as ASMR-experiencers. Mood, attention and personality characteristics were measured by the Profile of Mood States, the Flanker task and HEXACO. EEG, HR and EDA were recorded during the ASMR and control videos. Depressive feelings decreased after watching the ASMR video in individuals experiencing tingles relative to those not experiencing tingles. Furthermore, in all participants, irrespective of experiencing tingles, a decrease of HR during watching the ASMR video was found. In ASMR-experiencers scoring low on Conscientiousness EDA tended to increase and HR tended-relatively to the group not experiencing tingles—to decrease during watching the ASMR video. EEG recordings indicated that watching the ASMR video was associated with decreased alpha power in ASMR-sensitive participants and decreased theta as well as increased beta power in the whole group of participants. The observed ASMR-induced decrease of alpha and theta power and increase of beta power and (only in low conscientious participants) EDA may reflect that, apart from relaxation, ASMR is related to arousal and focused attention. Autonomous sensory meridian response (ASMR) is a sensory and emotional phenomenon, characterized by the experience of a ‘tingling’, static-like sensation, which is generally called paresthesia. The tingling can be felt at the scalp, back of the neck, upper spine, or in further bodily areas. The sensation is in response to specific audio -, visual -, or tactile stimuli, or even elicited by intentional attention control. These external  triggers include whispering, soft-speaking, tapping, scratching, slow and expert hand movements and close personal attention  occurred for audio trials when comparing ASMR to control participants and gamma waves (> 30 Hz) when comparing pre-tingle to post-tingle activity in ASMR participants. Higher levels of sensorimotor rhythm activity were also seen over the motor and somatosensory cortices during ASMR participants’ responses to ASMR-relevant audio files as compared to ASMR-irrelevant audio files. The authors conclude that increases of alpha, sensorimotor rhythm and gamma wave activity might reflect attentional and sensorimotor processes during ASMR in ASMR-sensitive individuals.To examine the relationship between ASMR and mindfulness, Roberts et al.  measuredIn a preceding study, a group of ASMR experiencers was compared with an age and gender matched control group on measures of absorption, flow and mindfulness. Results indicated no difference between ASMR-experiencers and control group concerning mindfulness, as measured by the MAAS. In addition, no correlation was found between ASMR characteristics determined with a self-designed ASMR questionnaire and scores on the MAAS. Notably, increased absorption was found in the ASMR group while no association between absorption and mindfulness was found. These results show that ASMR and mindfulness are different phenomena , relaxation, anxiety and stress . According to the inclusion criteria participants should only be healthy and aged between 17 and 35 years. No selection was made based on self-described ASMR experience nor were participants explicitly informed of the ASMR content of the study. The study included 33 female and 5 male participants. According to their own knowledge they were mentally and physically healthy. The participants were recruited by means of vu.sona-systems.com. This is a subject pool where students of the Vrije Universiteit may indicate to participate in research, which is rewarded by credits.An adapted ASMR-checklist of Barratt and Davis (Barratt and Davis https://www.youtube.com/watch?v=t0tSc-1qdKA). The control video lasted 3.04 min and included a neutral voice of a male, explaining how to make fresh pasta (https://www.youtube.com/watch?v=H8pAFgky-Xo). The video clips were presented on an iPad. To avoid disturbing the EEG recordings participants did not wear headphones but listened to the sounds from the iPad speaker. The order of the presentation of the videos was randomized.The ASMR and control video were the same standard ASMR and control (non-ASMR) video as used in study 2 by Poerio et al. . The ASMMood was measured by the Profile of Mood States (POMS) . Examples of adjectives are: ‘sad’ or ‘tired’. After adding 1 point to each rating, the ratings 1–5 of the items of a subscale are summed. The scales are composed of a different number of items, i.e., Anger contains 7 items (range of scores 7–35), Depression 8 items (range of scores 8–40), Tension 6 items (range of scores 6–30), Fatigue 6 items (range of scores 6 to 30) and Vigor 5 items (range of scores 5–25).The higher the scores on a scale are, the larger the emotional disturbance, except for vigor (higher score means more vigor). The Dutch POMS questionnaire is a reliable measurement with a Cronbach alpha for different scales, varying between 0.85 and 0.95 de Groot .Attention was measured by the Eriksen Flanker task (Eriksen and Eriksen r = 0.81), Conscientiousness (r = 0.76), Openness to experience (r = 0.74), Emotionality/Neuroticism (r = 0.52) and Agreeableness (r = 0.37). The self-report HEXACO-SPI consists of 104 items, like ‘My room is always cleaned up’. Participants indicate to what extent these statements are correct. Response options are 1 = totally disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = totally agree. Domains of the HEXACO-SPI are Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, Openness to experience. For the current study we analyzed the scores on Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to experience.Personality was measured by the HEXACO-Simplified Personality Inventory (HEXACO-SPI). The scale has an adequate factor structure, reliable domain scales (mean rXX = 0.79), more than adequate convergent validity and highly convergent construct validities. The HEXACO model is an extended version of the Big Five personality model . This system was developed at the Vrije Universiteit Amsterdam. For EDA recording we used electrodes from Biopac  combined with electrode gel, isotonic, 4OZ–GEL101. To measure the HR we used Kendall ARBO H98SG ECG electrodes. Two skin conductance electrodes were placed on the thenar eminence of the left hand and forearm, because the instruction manual recommends this configuration to attain optimal signal quality. Two HR electrodes were placed on the left forearm and under the right clavicle. The mean HR and EDA during the two videos were taken as output variables. The ECG had a sampling rate of 1000 Hz and HR was calculated based on time (in ms) between successive R waves  on the ECG to calculate average beats per minute. R peaks are automatically detected by the VU-AMS. Data were scored using the Data Analysis and Management Software . The average skin conductance, i.e. the average raw EDA signal, was used in this study. Skin conductance was sampled at a rate of 10 Hz with a signal range between 0 and 95 μS. The signal was filtered both in forward and reverse direction with a low pass filter with a cut off frequency of 2 Hz. Data processing from electrodermal signals was computationally performed with the DAMS. Clipping levels were automatically detected and removed from further analysis.Heart rate (HR) in beats per minute (bpm) and electrodermal activity (EDA) in microSiemens (µS) were measured by the VU-AMS  with the built in tool for spike analysis. The Persyst spike algorithm allows the detector to be extremely sensitive while maintaining a low false positive rate, and appeared to perform similar to human EEG readers. A detailed description of the algorithm and comparison with the performance of human EEG readers can be found in Scheuer et al. . For artFor the current study we selected the alpha (8.0–12.0 Hz), theta (4.0–8.0 Hz) and—to include sensorimotor rhythm-beta (12.0–25.0 Hz) frequency bands at channel F7, F8, P3, P4, T5 and T6. As electrodes T5 and T6 are also called parietal-temporal electrodes and have been renamed in the higher-resolution nomenclature  P7 and P8  and the difference scores. The effect of Group and Condition on RT of the Flanker task, HR, SCL and EEG were tested by mixed ANOVA. For the separate mixed ANOVAs in groups with high and low conscientiousness two groups were created by the SPSS procedure ‘visual binning’. We used the visual binning option ‘equal percentiles’ which resulted in one group consisting of participants with high scores and one group with low scores. As tension is known to be associated with a higher cardiovascular reactivity  and 2 groups , the obtained power was 0.89 for sample size = 12. With respect to the relationship between Group and Personality we conducted a power analysis for Pearson point biserial correlation using the program G* power. After applying a medium effect size r = 0.4  and a Mann–Whitney 4 McLeod  the obtaWe controlled for multiple comparisons of the frontal and temporal electrodes by applying Benjamini–Hochberg. An FDR of 0.05 can be too low in experiments of an exploratory nature McDonald . As EEG p < 0.05. Data were analyzed by using SPSS Statistics version 24.0.The hypotheses for mood, attention, HR, SCL, personality and EEG were tested two-tailed. The level of significance was set as T(36) = 1.71, p = 0.096) and gender (Χ2(1) = 2.09, p = 0.15) were not different between ASMR groups. To the question ‘how often do you look at ASMR videos?’ most participants answered ‘never’ . Notably, participants who indicated not to understand the abbreviation ASMR were verbally informed of its meaning. In addition, chi square tests indicated that the number of reported tingles were not significantly different between the group that first watched the control video and the group that first watched the ASMR video (Χ2(1) = 0.99, p = 0.32). As the number of participants reporting tingles (n = 6) in the group that first watched the control video was even smaller than that (n = 9) of the other group, the ASMR questionnaire presented after the control video seems not to induce ASMR expectancy effects.Independent t tests and chi square tests indicated that age . Participants who experience tingles showed a smaller change (mean rank = 14.67) in Depression than those who did not experience tingles (mean rank = 22.65). This result is shown in Fig. p > 0.05), but only under the ASMR condition (p = 0.043). However, this difference was not significant after Bonferroni adjustment for multiple tests. Moreover, Wilcoxon signed-rank tests did not indicate a significant change of Depression scores under the ASMR condition as compared to the control condition in the tingles group nor in the no tingles group (p > 0.05). There were no significant differences between Groups for Anger, Fatigue, Vigor and Tension (p > 0.05). The main effect of Condition (ASMR/control) on mood was tested with a Wilcoxon Signed Rank test. There was no significant effect of Condition on any of the five mood scales (p > 0.05). See Table The first hypothesis, ASMR improves mood, was tested by comparing difference scores (ASMR minus control) between Groups (tingles/no tingles) with a Mann–Whitney test. A significant effect was found for the difference scores of Depression (p > 0.05). This means that there was no difference between the groups in change of correct responses under ASMR and control condition. In addition, disregarding Group, a Wilcoxon Signed Rank indicated no significant effect of Condition for the number of correct responses on the Flanker task (p > 0.05).With respect to Attention, a non-parametric Mann–Whitney test did not show a significant difference between Groups (tingles/no tingles) for the difference scores of the number of correct responses of the Flanker task (p > 0.05). Besides that, there was no significant main effect of Condition on RT (p > 0.05). See Table To test the interaction effect of Group (tingles/no tingles) and Condition (ASMR/control) on RT of the Flanker task mixed ANOVA was applied with Group as between subjects factor and Condition as repeated measures factor. No significant interaction was found between Group and Condition  = 1.18, p = 0.284, η2 = 0.03, nor a significant main effect of Group  = 0.007, p = 0.934, η2 = 0.00. However, a significant main effect was found of Condition on HR, F = 9.85, p = 0.003, η2 = 0.22. The HR under the ASMR condition  was lower than that under the control condition . Spearman’s rank correlation indicated a trend of a positive relationship between HR and Tension scores for all participants only under the ASMR condition . In addition, mixed ANOVAs with Group (tingles/no tingles) and Condition were performed separately for high and low Conscientiousness. A marginal significant interaction between Group and Condition was found for low Conscientiousness, F = 3.32, p = 0.084, η2 = 0.15  as between subjects factor and Condition as repeated measures factor. With respect to the HR analyses data from one participant was excluded from data analysis due to noise in the recordings. Neither significant interaction was found between Group and Condition  = 1.14, p = 0.29, η2 = 0.03). Nor any significant main effect of Group  = 1.09, p = 0.30, η2 = 0.03 or Condition  = 0.27, p = 0.61, η2 = 0.01 on SCL was found. However, mixed ANOVA with Group (tingles/no tingles) and Condition performed separately for high and low Conscientiousness indicated a significant interaction between Group and Condition for low Conscientiousness, F = 5.05, p = 0.037, η2 = 0.21. Paired t tests indicated a marginal significant increase in average EDA under the ASMR condition as compared to the control condition in the tingles group (p = 0.077) while no  significant change was seen in the no tingles group (p = 0.95). These results are shown in  = 4.431, p = 0.044, η2 = 0.13 and at channel P4, F = 3.742, p = 0.063, η2 = 0.11. Figure t(12) = 2.322, p = 0.039) of the power of alpha at channel T6 and Fig. t(12) = 2.055, p = 0.062) of the power of alpha at channel P4 in participants who experience tingles as compared to the no tingles group (p > 0.05). However, no significant main effect was found for Condition on any channel of the alpha frequency band (p > 0.05).The effect of ASMR on the power of the EEG frequency bands , was tested with mixed ANOVA. With respect to the EEG analyses data from 6 participants were excluded from data analysis due to noise in the recordings. A  significant interaction effect of Group (tingles/no tingles) and Condition (ASMR/control) was found for the alpha frequency bands at channel T6, F = 4.510, p = 0.042, η2 = 0.13. Figure p > 0.05).A significant main effect of Condition on the beta frequency band was found at channel T5, F = 6.662, p = 0.015, η2 = 0.18 and channel T6, F = 16.554, p < 0.001, η2 = 0.36. Figure p > 0.05). All above mentioned results remained significant after controlling for the FDR by the Benjamini–Hochberg procedure. No significant main effects of Condition were found for other channels of the three frequency bands (p > 0.05). See Table Finally, a significant main effect of Condition was found on the theta frequency band at channel F7, rpb(36(38) = − 0.24, p = 0.14, Conscientiousness (rpb(38) = − 0.07 p = 0.69), Openness to experience (rpb (38) = − 0.16, p = 0.34), Neuroticism (rpb (38) = − 0.10, p = 0.51) and Agreeableness (rpb (38) = 0.009, p = 0.96). Means of HEXACO scales are shown in Table Finally it was analyzed if participants who score lower on Extraversion, Conscientiousness and participants who score higher on Openness, Neuroticism and Agreeableness are more sensitive to ASMR. This hypothesis was tested by calculating Pearson point biserial correlations between Group (tingles/no tingles) and scores on HEXACO scales. No significant correlations were found between Group (tingles/no tingles) and scores on Extraversion (The aim of the current study was to examine the effects of Autonomous Sensory Meridian Response (ASMR), in young adults, on mood, attention, heart rate, skin conductance, EEG frequency bands and the interaction with personality factors. The experience of ASMR was identified by tingling sensations while watching an ASMR video, measured by the ASMR-checklist . In the current study 15 (37%) of 38 participants experienced tingles while watching the ASMR video. These results indicate that not everyone is sensitive to ASMR. Notably, ASMR sensitivity has been found to be associated with a decreased connectivity between the frontal lobes and sensory and attentional areas  increase of depression in the no tingles group. Furthermore, across groups (tingles/no tingles) we found a decrease in HR while watching an ASMR video compared to watching a control video. With respect to EEG, the current results indicate a decrease of alpha power after watching an ASMR video in individuals who experience tingles compared to individuals who do not experience tingles. Besides that we found that across all participants the theta power decreases after watching an ASMR video compared to watching a control video. In addition, beta power was found to increase after watching an ASMR video. In contrast to the hypotheses, no effects of ASMR on attention and SCL were found. Furthermore, there was no significant relationship between tingles and personality traits.With respect to mood, we expected that the scores of the POMS would change in a positive direction after watching an ASMR video compared to a control video. The results indicate that feelings of depression decrease after watching an ASMR video in individuals who experience tingles as compared to those not experiencing tingles. This is in line with the results of Barratt and Davis  indicatiWith respect to attention, as measured with the Flanker task, we expected that watching an ASMR video would decrease the reaction time and increase the number of correct responses on the Flanker task. The relationship between ASMR and attention has not been studied before, but improved attention might be expected as a consequence of ASMR-induced changes in the state of flow. This is an absorbing mental state of deep focus and decreased awareness of the passage of time that is often associated with optimal cognitive performance, which can be considered to be a form of attention. Participants who scored higher on flow measures experienced more triggers which are associated with ASMR content, e.g., crisp sounds, slow movements  in the current study compared to that (n = 55) in the study of Poerio et al.  of lower extraversion scores in ASMR sensitive individuals. It must be noted that the HEXACO-SPI is not identical to the BFI as used by the previous cited studies. Although most HEXACO and BFI scales correlate well as is mentioned under the section Personality and means of the HEXACO scales as shown in Table n = 284, n = 83 and n = 146, respectively) were substantially larger than that of the present study (n = 15).Finally, no evidence was found for the hypothesis that individuals who are more sensitive to ASMR, as defined by experiencing tingles, score higher on Openness-to-Experience and lower on Conscientiousness (Fredborg et al. One limitation of the present study is the use of only one stimulus video. Presenting this particular ASMR video might have excluded ASMR-sensitive individuals who did not respond with tingles to the present stimulus selection. As is mentioned in the introduction ASMR trigger preferences vary across audio -, visual -, or tactile stimuli such as whispering, soft-speaking, tapping, scratching, slow and expert hand movements and close personal attention (Barratt and Davis In addition, the lack of baseline measurements and the use of only one video per condition does not allow for revealing potential differences in engagement or arousal elicited by the videos. As EDA, EEG and HR measures are sensitive to general arousal states, differences between conditions may be due to specific properties of the videos. However, as decreased alpha power under the ASMR condition was only found in participants experiencing tingles, this effect can be considered to be part of the ASMR experience. To clarify possible confounding effects of differences in video characteristics, future studies should include baseline measurements and measures of the engagement of participants with the stimuli.From the present results we may conclude that ASMR decreases feelings of depression in individuals who are sensitive to ASMR. Moreover, ASMR videos reduce heartrate in individuals, irrespective of being sensitive to ASMR or not. In addition, the EEG recordings of the present study show that ASMR videos decrease alpha and theta power and increases beta power, which may contribute to the idea that ASMR is related to arousal and a form of focused attention, like ‘state of flow’ or absorption.


The aim of this study is to explore the relationship between reproductive variables and the prevalence of metabolic syndrome (MetS) and its components among Chinese women aged 40 years and older.A cross‐sectional study was conducted among 4453 women aged 40 years and older in Guangxi, China. The associations between women's reproductive factors and MetS were analyzed using a logistic regression model.The prevalence of MetS was 23.9% in this population. Women with MetS were mostly older, more likely to be postmenopausal, and had higher parity. Compared to women with one prior live birth, those with three or more live births had the highest odds of having MetS . Similarly, compared to premenopausal women, postmenopausal participants had higher odds of having MetS . No associations were observed between MetS and abortion or with age at menarche.Our study suggests that multiparity and menopausal status may be associated with the development of MetS. The inconsistency seen in epidemiological research to date calls for further investigation. HighlightsReproductive variables are associated with metabolic syndrome (MetS) and its components, but the results are conflicting.In this cross‐sectional study involving 4453 women over 40, we found that reproductive variables, including menarche, pregnancy, childbirth, breastfeeding, and menopause, may play a role in modulating the risk of MetS.Simple information on reproductive history could help identify women who might have a higher risk of metabolic disorder in their later life and prevent the subsequent related chronic diseases. Its incidence parallels with obesity and type 2 diabetes (T2DM), increasing the risk for atherosclerosis and cardiovascular disease (CVD).Pregnancy, in particular, is a period when the levels of estrogen and progesterone increase rapidly along with significant changes in the physiological functions of women's bodies, some of which may lead to a higher risk of adverse metabolic health outcomes in their later life.Menopause, a phase when ovarian function declines and estrogen levels drop,To our knowledge, only a few studies have focused on every aspect of the reproductive factors or all individual components of MetS, even though previous studies revealed that the reproductive stage affects women's long‐term metabolic health.22.1n = 3447), (2) women under 40 years old (n = 75), (3) women without data of one or more components of MetS (n = 345), (4) women who did not provide a history of pregnancy or breastfeeding (n = 232), and (5) those women who lacked information on age at menarche or unnatural menopause (n = 473). A total of 4453 participants were included in the final analysis. The REACTION study was approved by the Committee on Human Research at Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine and was conducted in accordance with the institutional guidelines. All study participants provided written informed consent , which was conducted among 259 657 adults aged 40 years or older in 25 communities across mainland China during 2011–2012.2.22). WC was measured at an umbilical level in the standing position. An automated electronic device  was used to measure SBP and DBP in the sitting position three times consecutively at 1‐min intervals each.Participants were scheduled for a personal interview and a clinic visit after the recruitment, which took place at the First Affiliated Hospital of Guangxi Medical University. Body height, weight, waist circumference (WC), hip circumference (HC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured by experienced nurses according to a standard protocol. Body mass index (BMI) was calculated as body weight in kilograms divided by height in meters squared , low‐density lipoprotein cholesterol (LDL‐C), HDL‐C, and triglycerides (TG) were measured with an autoanalyzer . The level of glycosylated hemoglobin (HbA1c) was determined by high‐performance liquid chromatography  using the VARIANT II Hemoglobin Testing System certified by the National Glycohemoglobin Standardization Program. After obtaining fasting plasma glucose (FPG) and fasting serum insulin by the methods mentioned above, the fasting insulin resistance index (FIRI) was calculated.2.4Reproductive factors assessed in this study included gravidity, parity, abortion, history of breastfeeding, menopausal status, and age at menarche. The number of abortions was defined by the total number of self‐reported spontaneous and induced abortions. Information on the mean duration of breastfeeding per child was calculated as the sum of the durations divided by parities. Menopausal status was defined by the following questions: “Are you still menstruating? If yes, when was the last time you had your period?” Women who answered “no” were categorized as menopausal. Age at menarche was determined by asking, “How old were you when you first got your period?”The National Cholesterol Education Program Adult Treatment Panel III (NCEP‐ATP III) was used to define MetS in this study. This definition requires the individual to have at least three of the following risk factors2, (2) TG ≥ 1.70 mmol/L, (3) HDL‐C < 0.90 mmol/L for men and <1.00 mmol/L for women, (4) SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or current use of antihypertensive medication, and (5) FPG ≥ 6.1 mmol/L or 2‐h postprandial blood glucose (2hPG) ≥ 7.8 mmol/L or previous diagnosis of T2DM. We also performed a sensitivity analysis using the International Diabetes Federation (IDF) criteria. This definition requires an increased WC (for men ≥94 cm and for women ≥80 cm) plus at least two of the followingWe added a sensitivity analysis using the Chinese Diabetes Society (CDS) criteria. This definition requires the individual to have at least three of the following risk factors2.5All statistical analyses were performed using SAS software version 9.4 for Windows . In the present study, gravidity was divided into four groups: 1, 2, 3, and >3 times. Parity was categorized as: none, 1, 2, and ≥3 prior live births. Breastfeeding duration was referred to as the average period each child was breastfed: never, 1–6, 7–12, and >12 months. Abortions were categorized as: never, 1, 2, and ≥3 times. The age at menarche was evaluated in years: ≤13, 14–15, and ≥16 years old. Menopausal status was categorized as “yes” and “no.”t test and chi‐squared test, respectively. The prevalence of MetS and its five components in different groups of reproductive variables was also assessed. Based on the findings detailed in the literature, the variables described below were considered as potential confounders and were included in our analysis: age , ethnic group (Han or the Minority), smoking (yes/no), alcohol drinking (yes/no), education level (below high school and high school or above), and physical activity (never or mild and moderate or above). Detailed information on some of these variables has been described above. The missing values were categorized into an additional category in the analysis. The associations between reproductive factors and MetS and all of its individual components were analyzed using logistic regression models. We implemented two logistic regression models to assess the relationship between reproductive factors and MetS. In model 1, no covariates were adjusted. In model 2, age, ethnic group, smoking status, alcohol drinking, education, physical activity, and BMI were further adjusted. We used odds ratios (OR) and 95% confidence intervals (CI) to estimate the strength of this association.All continuous variables are presented as means and standard deviations (± SD) or medians and interquartile ranges, and all categorical variables are expressed as numbers and proportions. A comparison of means and proportions was performed with the independent Following the first diagnostic criteria proposed by the World Health Organization (WHO) in 1998, the NCEP‐ATP III and the IDF have successively proposed their own diagnostic criteria.2 to define overweight and 28.0 kg/m2 to define obesity, we chose the cutoffs of 24.0 kg/m2 as the grouping basis.2 and BMI < 24 kg/m2.The prevalence of MetS was highly influenced by age, and data showed that compared with women aged 40–59 years, women over 60 years were twice more likely to meet the MetS criteria.33.1Among the 4453 women eligible for the analysis , 1062 women reported MetS. Table 0.7 years3.2The relationship between reproductive variables and the prevalence of MetS as well as its individual components is presented in Figure 3.3We utilized multivariate logistic regression analysis to evaluate the association between reproductive factors and MetS and its components. Compared to the women with one pregnancy, those who had three pregnancies had approximately 42% higher odds of having MetS  was enhanced by reproductive factors Figure . Results2, that is, the overweight or obese subgroup, the results also tended in the same direction as in the primary analysis , the National Health Commission of China Public Welfare Research Project (201502007), the “Excellent Medical Talent” Research and Innovation Ability Training Project of the First Affiliated Hospital of Guangxi Medical University (2017025), and the Innovation Project of Clinical Research Climbing Plan of the First Affiliated Hospital of Guangxi Medical University (YYZS2020012).The authors have no conflict of interest relevant to the article.Figure S1. Odds ratios (ORs) for the association between reproductive variables and abdominal obesity. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S2. Odds ratios (ORs) for the association between reproductive variables and elevated TG. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S3. Odds ratios (ORs) for the association between reproductive variables and low HDL‐C. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S4. Odds ratios (ORs) for the association between reproductive variables and elevated blood pressure. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S5. Odds ratios (ORs) for the association between reproductive variables and impaired fasting glucose. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S6. Odds ratios (ORs) for the association between reproductive variables and overall MetS after exclusion for CVD and T2DM participants. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S7. Odds ratios (ORs) for the association between reproductive variables and overall MetS using the CDS criteria. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S8. Odds ratios (ORs) for the association between reproductive variables and overall MetS using the IDF criteria. Model 1 (unadjusted), model 2 .Click here for additional data file.Figure S9. Odds ratios (ORs) for the association between reproductive variables and overall MetS in the subgroup analysis by age. Model 1 (unadjusted), model 2 . Figure S9‐1. Aged 40 to 59 years Figure S9‐2. Over 60 years oldClick here for additional data file.Figure S10. Odds ratios (ORs) for the association between reproductive variables and overall MetS in the subgroup analysis by BMI. Model 1 (unadjusted), model 2 . Figure S10‐1. BMI < 24 kg/m2Figure S10‐2. BMI ≥24 kg/m2Click here for additional data file.


Cardiovascular risk factors and comorbidities are highly prevalent among COVID‐19 patients and are associated with worse outcomes.We therefore investigated if established cardiovascular risk assessment models could efficiently predict adverse outcomes in COVID‐19. Furthermore, we aimed to generate novel risk scores including various cardiovascular parameters for prediction of short‐ and midterm outcomes in COVID‐19.We included 441 consecutive patients diagnosed with SARS‐CoV‐2 infection. Patients were followed‐up for 30 days after the hospital admission for all‐cause mortality (ACM), venous/arterial thromboembolism, and mechanical ventilation. We further followed up the patients for post‐COVID‐19 syndrome for 6 months and occurrence of myocarditis, heart failure, acute coronary syndrome (ACS), and rhythm events in a 12‐month follow‐up. Discrimination performance of DAPT, GRACE 2.0, PARIS‐CTE, PREDICT‐STABLE, CHA2‐DS2‐VASc, HAS‐BLED, PARIS‐MB, PRECISE‐DAPT scores for selected endpoints was evaluated by ROC‐analysis.Out of established risk assessment models, GRACE 2.0 score performed best in predicting combined endpoint and ACM. Risk assessment models including age, cardiovascular risk factors, echocardiographic parameters, and biomarkers, were generated and could successfully predict the combined endpoint, ACM, venous/arterial thromboembolism, need for mechanical ventilation, myocarditis, ACS, heart failure, and rhythm events. Prediction of post‐COVID‐19 syndrome was poor.Risk assessment models including age, laboratory parameters, cardiovascular risk factors, and echocardiographic parameters showed good discrimination performance for adverse short‐ and midterm outcomes in COVID‐19 and outweighed discrimination performance of established cardiovascular risk assessment models. Arterial hypertension, hyperlipidemia, diabetes, history of or current smoking, and history of coronary artery disease (CAD) are the comorbidities most often identified in COVID‐19 patients.Due to enormous pro‐thrombotic and fibrinolytic imbalance in COVID‐19, a significantly higher prevalence of thromboembolic complications compared to other critical illnesses is reported.Arterial thrombotic events seem to be less common than venous thromboembolism (VTE). According to Klok et al.,d‐dimer showed good discrimination performance for mechanical ventilation and ACM in a 30‐day follow‐up period. However, echocardiographic parameters failed to discriminate between favorable and adverse outcomes in COVID‐19 patients.A variety of risk assessment models for prediction of mortality and/or ICU treatment among COVID‐19 patients exist, for example, the Quick COVID‐19 Severity Index (qCSI), COVID‐GRAM, 4C Mortality Score.2DS2‐VASc score assessing the risk of stroke and thromboembolism in patients with atrial fibrillation (AF).Previous data strongly suggest an association between cardiovascular burden and increased mortality in COVID‐19. Hence, we sought to examine whether existing risk assessment models, originally established to predict the risk of thromboembolic and bleeding complications in CVD patients, may help to identify COVID‐19 patients at high risk for an unfavorable course of disease. We evaluated GRACE 2.0 score which was developed on the basis of a global registry including 102.341 acute coronary syndrome (ACS) patients and aims to predict the risk of ACM up to 3 years after an ACS as well as the risk of myocardial infarction (MI) after 1 year.After addressing the available risk scores our further goal was to generate risk assessment models including cardiovascular comorbidities, cardiac biomarkers, and echocardiographic parameters to predict adverse short‐ and midterm outcomes in patients with SARS‐CoV‐2 infection.22.1About 441 consecutive patients, hospitalized for at least 24 h and tested positive for SARS‐CoV‐2 at the University Hospital Tübingen, Germany, from February 2020 until January 2021, were enrolled in this retrospective study. A positive polymerase chain reaction (PCR) test for SARS‐CoV‐2 was the only inclusion criterium, irrespective of the reason for hospital admission. Hospitalized patients which were tested positive for SARS‐CoV‐2 in the course of the hospital stay were also included in the study. Written informed consent was obtained wherever possible. Transthoracic echocardiography (TTE) and electrocardiogram were performed in all patients, if considered clinically indicated. Patients were followed up for 30 days, 6, and 12 months after hospital admission for different study endpoints, irrespective of the duration of the hospital stay. Combined endpoint consisted of first manifestation of either ACM and/or venous/arterial thromboembolism. Venous thromboembolic complications were considered as the occurrence of PE, DVT, venous occlusive disease (VOD), and sinus thrombosis, whereas arterial thrombotic events included AMI, ischemic stroke (IS), and peripheral limb ischemia. Disseminated intravascular coagulation (DIC) was defined as a hypercoagulable based on thromboelastographic findings without a diagnosed thrombosis or bleeding complication and also included in the combined endpoint.The study was approved by the institutional ethics committee (238/2018BO2) and complies with the declaration of Helsinki and the good clinical practice guidelines.2.2SARS‐CoV‐2 virus was detected from nasopharyngeal secretions using a real‐time reverse transcriptase PCR. Acute respiratory distress syndrome (ARDS) was diagnosed based on the Berlin Definition of ARDS.2.3n = 374, 84.8%) and TTE was available in 266 (60.3%) patients in the course of the hospital stay. Peripheral venous blood was drawn for laboratory parameters in the first 24 h after the positive test for SARS‐CoV‐2, if available. Certain laboratory parameters were, however, available only in the further course of disease and the first available values were included in the analysis.Chest X‐ray and/or a thoracic computed tomography were performed in symptomatic SARS‐CoV‐2 patients (2.42‐DS2‐VASc), and bleeding complications  were calculated in the study cohort.Available risk scores for assessment of ACM and/or myocardial infarction (MI) , ischemic stroke , predictive performance of the risk assessment models was considered very poor (<0.5), poor (0.5.–0.7), good (>0.7), very good (>0.8), or excellent (>0.9). For newly established scores, the leave‐one‐out method  was applied to avoid overoptimism. Established scores were analyzed without further correction. Multiple imputations were applied for missing predictor variables. Thus, within each of the 100 imputations, 411 leave‐one‐out steps were performed using variable selection which resulted in 41 100 different models. Proposed risk assessment scores were obtained from the averaged imputation samples, the AUCs were obtained by averaging results from 100 imputations, standard errors were obtained using Rubin's formula.Statistical analysis was performed using IBM SPSS Statistics, version 26.0. Continuous variables are presented as mean values (±standard deviation) and compared using Student's 3The demographic and clinical characteristics of the patient cohort are listed in Table The distribution of the study endpoints at 30 days, 6, and 12 months after hospital admission is shown in Table Table Supporting Information: Table d‐dimer, CRP, procalcitonin, LDH, age, and reduced RV‐function . ACM was best predicted by a combination of d‐dimer, CRP, LDH, NT pro‐BNP, age, moderate/severe TR, and arterial hypertension . A combination of three laboratory parameters  best predicted venous/arterial thromboembolism . A model including CRP, LDH, troponin I, age, and arterial hypertension reached an AUC = 0.86, 95% CI 0.82–0.90, when predicting mechanical ventilation. Another three risk assessment models could successfully predict myocarditis (AUC of 0.98 [95% CI 0.96 to <1.0]), ACS , heart failure , and rhythm events . The occurrence of post‐COVID‐19 syndrome could be predicted with an AUC = 0.65, 95% CI 0.57–0.73. The observed sensitivity of selected scores is represented in Supporting Information: Table Figure Table 4d‐dimer, CRP, PCT, TnI, LDH, and NT pro‐BNP, GPT could successfully predict combined endpoint, ACM, venous/arterial thromboembolism and need for mechanical ventilation in a 30‐day follow‐up, whereas myocarditis, ACS, heart failure, rhythm events in a 12‐month follow‐up; (4) prediction of occurrence of the post‐COVID‐19 syndrome in our cohort was poor.The main findings of the present study are: (1) risk scores originally established for CAD patients showed better prediction performance for combined endpoint and ACM compared to thromboembolism and mechanical ventilation in the current cohort; (2) GRACE 2.0 score showed best discrimination performance for combined endpoint and ACM when compared to other selected risk assessment models; (3) risk scores including age, reduced left or right ventricular function, moderate/severe tricuspid regurgitation, systolic pulmonary artery pressure, arterial hypertension, Our study aimed to evaluate the predictive performance of pre‐existing cardiovascular risk assessment models and to generate risk assessment scores, including cardiovascular parameters, to predict ACM, venous/arterial thromboembolism, and/or mechanical ventilation in a short‐term follow‐up of 30 days in a cohort of 441 COVID‐19 patients. We also aimed to generate risk assessment models which could predict the occurrence of post‐COVID‐19 syndrome in a 6‐month follow‐up and cardiovascular complications  in a 12‐month follow‐up.CVRFs and comorbidities are associated with increased mortality and worse outcomes in COVID‐19.n = 2) of events observed in our cohort. The generated risk assessment models included age, reduced left ventricular ejection fraction, reduced right ventricular function, moderate/severe tricuspid regurgitation, systolic pulmonary artery pressure, arterial hypertension, d‐dimer, CRP, PCT, TnI, LDH, GPT, and/or NT pro‐BNP. In the context of other risk assessments scores developed for COVID‐19 patients, our risk models showed similar prediction performance. Compared to the 4C Mortality Score based on a cohort of over 57 000 patients in the United Kingdom and developed for prediction of intra‐hospital mortality (AUC = 0.79) our model predicted ACM at 30 days slightly better (AUC = 0.827).To identify potential parameters which could be included in risk assessment models for COVID‐19, we performed ROC‐analyses with laboratory markers, echocardiographic parameters, and CVRFs in our COVID‐19 cohort. Laboratory parameters (cardiac and inflammatory markers) showed good (AUC > 0.7) predictive performance for combined endpoint, ACM, venous/arterial thromboembolism and mechanical ventilation in a short‐term follow‐up, and cardiovascular complications  in a 12‐month follow‐up, whereas independent cardiovascular risk factors/comorbidities and echocardiographic parameters failed to discriminate the study endpoints. However, combining laboratory with demographic, echocardiographic parameters, and cardiovascular risk factors/comorbidities revealed several models with very good (AUC > 0.8) discrimination of combined endpoint, ACM, mechanical ventilation, myocarditis, and ACS, and good (AUC > 0.7) predictive performance for venous/arterial thromboembolism, heart failure, and rhythm events. However, the predictive performance of myocarditis should be interpreted with caution due to a very low number (As associations between cardiac comorbidities and poor COVID‐19 prognosis are evident, we expected CVRFs to have significant influence on outcomes. In our cohort, however, only arterial hypertension was identified as a significant predictor of ACM and mechanical ventilation. A score by Galloway et al. and VACO‐index also identified diabetes mellitus as a good discriminator for prediction of ICU admission and ACM.The finding, that beyond inflammatory parameters also cardiac biomarkers and echocardiographic parameters may serve as significant predictors of poor outcomes corresponds to current evidence suggesting that right ventricular dysfunction is associated with worse prognosis in COVID‐19.5As cardiovascular risk factors and comorbidities are highly prevalent in COVID‐19 and associated with poor prognosis, we aimed to establish risk assessment models including cardiovascular biomarkers, risk factors, and echocardiographic parameters to predict adverse short‐ and midterm outcomes in COVID‐19. Risk scores including laboratory, demographic, as well as echocardiographic parameters and cardiovascular risk factors showed markedly better performance in prediction of ACM, venous/arterial thromboembolism and need for mechanical ventilation in COVID‐19 compared to established cardiovascular risk assessment models.6Limitations of this study include the moderately sized patient cohort used for generation of the risk assessment models. Patients included in the study cohort were potentially different in terms of co‐morbidities before SARS‐CoV‐2 infection which could have had a severe impact on their prognosis. A limitation of our study is that we cannot discriminate between pre‐existing or SARS‐CoV‐2‐induced elevated pulmonary artery pressure, heart failure, elevated NT pro‐BNP, and so on. However, even if not directly caused by COVID‐19, parameters included in our models show good discriminatory performance for the pre‐defined endpoints and thus remain, in our opinion, important factors for prognosis in COVID‐19 patients. Furthermore, this is a retrospective single‐center study that lacks an external validation cohort. As the vaccination against COVID‐19 started in January 2021 and we recruited patients for the current study in the period from February 2020 until January 2021, we cannot deliver information considering the vaccination status of the patients included in the study. Finally, not all of the parameters included in the study were available for all patients.Monika Zdanyte: Data collection, statistical analysis, drafting of the manuscript. Peter Martus: Expert statistical analysis, critical revision of the manuscript. Jeremy Nestele, Alexander Bild, Lars Mizera, Andreas Glatthaar, Álvaro Petersen Uribe, Frederic Emschermann, and Jessica‐Kristin Henes: Data collection, critical revision of the manuscript. Tobias Geisler, Karin Müller, and Meinrad Gawaz: Critical revision of the manuscript. Dominik Rath: Study concept, data collection, critical revision of the manuscript.The authors declare no conflicts of interest.Supporting information.Click here for additional data file.


Literature on severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection in cancer patients is scarce in Latin America. This population seems to have a higher risk for adverse outcomes. This study aims to correlate clinical characteristics with outcomes in patients with cancer.We included all patients with cancer and confirmed SARS‐CoV‐2 infection from April 19 to December 31, 2020, at the Instituto Nacional de Cancerologia, Mexico. Clinical information was obtained from medical and epidemiological records. For the association between variables and hospitalization, invasive mechanical ventilation (IMV), and mortality, univariate and multivariate logistic regression were performed; odds ratios and 95% confidence intervals were calculated.p < 0.01), male gender (p = 0.03), hematological malignancies (HM) (p = 0.04) and advanced cancer (p = 0.03) increased the risk for hospital admission. Forty‐five (10%) patients required IMV. Age (p = 0.02); DM (p = 0.04); high C‐reactive protein (p < 0.01), and lactate dehydrogenase (p = 0.03) were associated with IMV. Mortality within 30 days after diagnosis was 18% (76 cases). Associated characteristics were age (p = 0.04) and low albumin (p < 0.01).Four hundred thirty‐three patients were included; 268 (62%) were female, the median age was 55 years. One hundred thirty‐five (31%), 131 (30%), and 93 (21%) patients had obesity, hypertension, and diabetes mellitus (DM), respectively. Three hundred forty‐one (79%) had solid cancer. One hundred seventy (39%) had advanced cancer. Two hundred (46%) patients were hospitalized. Age (In this study, patients with cancer showed higher mortality, need for hospitalization, and IMV compared with other non‐cancer cohorts. We did not find an increased risk in mortality for HM. Although our cohort was younger than others previously reported, age was a strong predictor of adverse outcomes. Variables associated with IMV and death were similar to those previously described in cancer patients with COVID‐19. In this study, we found that patients with cancer had higher mortality, need of hospitalization, and mechanical ventilation compared with other non‐cancer cohorts. Age was the most important risk factor for hospital admission, invasive mechanical ventilation, and death regardless of the type of cancer. This infection has rapidly spread across the globe since December 2019, when it was first detected in Wuhan, China.SARS‐CoV‐2 infections in Latin America and the Caribbean represent 12% of recent global infections; this region includes eight of the 10 countries with the highest death rate worldwide.In patients living with cancer, there is controversial evidence about the impact of cancer on the evolution of COVID‐19. These patients have been previously recognized as a susceptible population because of immunosuppression status and treatment side effects that may contribute to developing severe forms of infection.A detailed epidemiological, symptoms, and cancer stage assessment is necessary to evaluate and manage these patients. Therefore, this study aims to describe the clinical characteristics, outcomes, and 30 days all‐cause mortality of patients with cancer and SARS‐CoV‐2 infection in a Mexican referral cancer center.2We conducted a retrospective analysis of patients with cancer and confirmed SARS‐CoV‐2 infection from April 19 to December 31, 2020, at the Instituto Nacional de Cancerologia (INCan), a 133‐bed referral, teaching hospital for adult patients with cancer, in Mexico City.In March 2020, INCan increased its surge capacity for COVID‐19; an exclusive closed 16‐bed isolated ward and an 8‐bed intensive care unit for COVID‐19 patients were conditioned, along with an exclusive triage and ambulatory respiratory attention unit.All patients with solid or hematological malignancies (HM) and positive laboratory‐confirmed SARS‐CoV‐2 test by real‐time polymerase chain reaction (RT‐PCR) from nasopharyngeal swabs were included.Information about demographic data, clinical characteristics, comorbidities, oncologic history, Eastern Cooperative Oncology Group performance status (ECOG), oncological treatment, lung computed tomography (CT), laboratory data, co‐infections, treatment for COVID‐19, and outcomes were obtained from epidemiological registries, electronic medical records, and the Microbiology Laboratory. RT‐PCR testing was performed at Instituto Nacional de Enfermedades Respiratorias (INER), a reference laboratory for SARS‐CoV‐2 testing. There was no formal sample size determination, and all patients who met the inclusion criteria were recruited.Ethics approval was obtained from the Institutional Board Review (Rev/0016/20). Written informed consent was waived because of the nature of data and the unprecedented nature of the COVID‐19 pandemic. Patient confidentiality was protected, and information was secured.We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statements to report the findings.2.1A case of SARS‐CoV‐2 infection was defined as a patient with cancer and positive RT‐PCR assay from a nasopharyngeal or oropharyngeal swab. COVID‐19 englobes patients with SARS‐CoV‐2 infection and acute respiratory illness.The type of malignancy was divided into two categories: solid tumors and hematologic neoplasm. According to the clinical stage, patients were classified as having advanced cancer if metastatic disease or cancer recurrence were documented at SARS‐CoV‐2 diagnosis. Hematologic malignancies that are not anatomically staged were considered disseminated at diagnosis. Patients on current treatment were further classified if they received cytotoxic agents, immunotherapy (immune checkpoint inhibitors or immune system modulators), or molecular targeted therapy . In the case of patients receiving combined regimens of monoclonal antibodies, such as rituximab plus cytotoxic agents  were classified as part of the cytotoxic chemotherapy group. The functionality was measured through ECOG calculated at the last clinical visit.The follow‐up time was calculated as the difference between the day of COVID‐19 diagnosis and the patients ‘death or last visit, accordingly. Overall and in‐hospital mortality were calculated. Overall mortality was calculated as the number of deaths in patients with COVID‐19 that occurred for the entire cohort. In‐hospital mortality was calculated as the number of deaths during the hospitalization in patients with COVID‐19 in relation to all patients admitted with cancer and COVID‐19.Best supportive care (BSC) was defined in this study as the group of patients who, at diagnosis of SARS‐CoV‐2 were receiving only supportive treatment, without anticancer therapy, and upon COVID‐19 diagnosis, were deemed to have an overall survival of less than 3‐months and were not candidates for invasive interventions. For patients in BSC with respiratory distress and medical criteria for invasive mechanical ventilation (IMV), do not resuscitate orders for terminal sedation were provided.2.2We conducted a descriptive analysis; quantitative variables were presented according to distribution as medians and interquartile range (IQR); for qualitative variables, frequencies, and percentages were calculated.p < 0.200 were introduced into a multivariate logistic regression analysis to address confounders. Adjusted odds ratios (aOR) with 95% confidence intervals (95% CI) were used to establish the association. For in‐hospital mortality analysis, we excluded patients under BSC. In the analysis for IMV, patients with do‐not‐resuscitate orders were excluded.Univariate logistic regression was performed to evaluate risk factors for three main outcomes: hospital admission, IMV, and 30‐day mortality. Variables with a SPSS Statistics 25.0 software was used for statistical analysis and graphics and tables were made in Prism version 9.3During the study period, a total of 433 patients were diagnosed with SARS‐CoV‐2. Two hundred sixty‐eight (62%) were women, and the median age was 55 years (IQR 44–63).A high proportion of patients had a history of chronic diseases, 129 (30%), 73 (17%), 28 (7%) had one, two, or more than three comorbidities, respectively. Ninety‐three (21%) had diabetes mellitus (DM); 131 (30%) hypertension, and 135 (31%) obesity. One hundred thirty‐seven (32%) patients had a current or previous smoking history.Three hundred thirty‐nine (78%) patients presented respiratory symptoms at diagnosis, and 94 (22%) were asymptomatic; 6 (8%) patients of the asymptomatic group progressed to symptomatic infection during the following days. The most common symptoms at the onset of illness were cough (54%), fever (43%), dyspnea (37%), and headache (32%).In 313 (72%) patients, a thoracic CT was performed. The most prevalent findings were ground‐glass infiltrates in 257 (82%) patients, followed by consolidation in 120 (38%), nodules in 113 (36%), and pleural effusion in 42 (13%). One hundred seventy‐six (56%) had bilateral affection.3.1n = 81, 19%); lymphoma ; cervical , and colorectal . For the stage of cancer, 170 (39%) had advanced cancer. HM were associated with symptomatic SARS‐CoV‐2 infection .Three hundred forty‐one (79%) had a diagnosis of solid neoplasm, 82 (19%) HM, and 10 (2%) patients were under study for diagnosis and cancer staging. The most common neoplasms were breast (One hundred thirty‐four patients (31%) were receiving systemic therapy at COVID‐19 diagnosis; 109 (25%) with cytotoxic chemotherapy, and 25 (6%) with immunotherapy or targeted therapy.status of 0–1, 2, and 3–4 were observed in 271 (63%), 105 (24%), and 54 (12%) patients, respectively. ECOG information was not available in 3 (1%) patients.ECOG 3.2E. coli (17%), Pseudomonas spp. (14%), and Stenotrophomonas spp. (14%). Fifteen (3%) patients were treated with amphotericin or voriconazole because of high clinical or radiological suspicion of aspergillosis; only two (13%) were confirmed by positive culture.Secondary bacterial pneumonia, abdominal sepsis, and bacteremia or invasive fungal infections were documented in 36 (8.3%) patients. The most frequent microorganisms were: p < 0.01); women ; hematologic malignancy  and advanced cancer   Figure .p = 0.02); DM ; C‐reactive protein (mg/dl)  and lactate dehydrogenase (LDH) (μmol/(h ml) )  patients had pulsed oximetry <90% and 45 (10%) required IMV during hospitalization. Variables associated to IMV were age per 10 years increase  Figure .p = 0.04) and hypoalbuminemia   patients: 29 (38%) were under terminal sedation. Sixty‐nine (35%) patients died during the hospital stay. Risk factors related with mortality were age, per 10 years increase  Figure .p = 0.17) and overall, 30‐day mortality  when comparing the first and second half of 2020 (April–July vs. August–December).We did not find differences in the proportion of patients admitted to the hospital , the LEOSS registry, or the UKCCMP cohort, which are multicenter studies including mainly centers from the USA, Canada, the United Kingdom, and Western Europe.There was a high prevalence of obesity, DM, and hypertension in our patients, consistent with the prevalence described in the non‐cancer population in MexicoA majority of our patients had solid tumors, similar to other series in patients with cancer and COVID‐19.Regarding the clinical presentation, 22% of our patients were asymptomatic. The latter may be explained by the early initiation of the screening program for patients undergoing elective surgery and, to a lesser extent, detecting suspicious cases through CT or Positron Emission Tomography (PET) studies for unrelated reasons to COVID‐19. The percentage is lower compared to that for the general population (40%).Concerning the clinical presentation of COVID‐19, 35% of our patients presented oxygen saturation less than 90% during their first medical evaluation, the double compared to the LEOSS registry.The 30‐day mortality rate was almost 20%. This mortality rate is twice the overall COVID‐19 related mortality reported in the general Mexican population (9%)Regarding risk factors related to the different outcomes, DM, age, high C‐reactive protein, and LDH were associated with an increased risk of IMV. Many studies report comorbidities to be associated with severe disease. Type 2 DM has been reported as a risk factor for severe COVID in the general Mexican population.Regarding age, there was a strong association of older age with hospitalization and good evidence of an association with IMV and mortality. Age has been reported as a risk factor in many cohorts. In the COVID‐19 Cancer and Consortium (CCC19), factors associated with mortality were age, male sex, number of comorbidities, and active cancer.Hypoalbuminemia has been associated with critically ill patients and mortality in several studies.We did not find an association between cytotoxic chemotherapy or targeted therapy with the outcomes measured. The UKCCMP also explored previous cancer therapies as a risk of severe COVID, but neither cytotoxic chemotherapy in the last 4 weeks nor targeted therapy, immunotherapy, or radiotherapy were associated with mortality.Regarding COVID‐19 treatment, access to COVID‐directed treatment in Mexican institutions has been mainly concentrated in research protocols or COVID‐reconverted hospitals, and most COVID‐19 treatments have relied on steroids. Steroids have shown an impact on mortality in patients with COVID‐19 with oxygen requirements.This is the first study describing a large cohort of cancer and COVID‐19 throughout the COVID‐19 spectrum and different outcomes in Mexico, and one of the few in Latin America. We report a relatively young cohort of patients with advanced cancer, predominantly female, reflecting the epidemiology of cancer in our country and many other countries in the region. Mortality was similar to that reported in high‐income countries, with age and low albumin as the main factors associated with 30‐day mortality. Cancer‐related factors were not found to be associated with a worse prognosis.4.1This study has some limitations. It is a retrospective study with data collected as the pandemic was ongoing, and we cannot ascertain the causality of all the associations found. Data reported here only represents one center, so the results might not be generalizable to the whole population. However, the INCan is a referral cancer center receiving patients from many country regions. Also, we did not look for specific interaction effects that could have affected the interpretation of some of the associations.No conflict of interests was declared by the authors.Daniel de‐la‐Rosa‐Martinez: SCD, DA, AI, ME, FA. Mercedes Aranda‐Audelo: SCD, DA, AI, ME, FA. Alexandra Martin‐Onraet: SCD, DA, AI, ME, FA. Beda Islas‐Muñoz: DA, ME, FA. Carolina Perez‐Jimenez: DA, ME, FA. Pamela Alatorre‐Fernandez: DA, ME, FA. Patricia Cornejo‐Juarez: SCD, DA, AI, ME, FA. Erika Ruiz‐Garcia: SCD, DA, ME, AI. PA. Paola Zinser‐Peniche: DA, AI, FA. Luis Ortiz‐Luna: DA, AI, FA. Abelardo Meneses‐Garcia: ME, AI, FA. Angel Herrera‐Gomez: AI, ME, FA. Diana Vilar‐Compte: SCD, DA, AI, ME, FA, PA.The study was approved by the institutional review board (Rev/0016/20). Written informed consent was waived because of the nature of data and the unprecedented nature of the COVID‐19 pandemic. Patient confidentiality was protected, and information was secured.


CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross‐validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand–kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer‐aided drug discovery has been proven a useful and cost‐effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin‐dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand–kinase inhibition constants (pK To date, 71 inhibitors have been approved by the U.S. Food and Drug Administration, targeting a small fraction of human kinases.Cyclin‐dependent kinases (CDKs) within the family of Ser/Thr kinases can drive the cell cycle propagation upon bindings to cyclins. They have become popular chemotherapeutic targets for different types of cancers. While a number of studies have been focused on CDK4/6 inhibitors to mediate tumor cell cycle arrest, CDK2 can also be a promising target to overcome drug resistance to CDK4/6 inhibitors.i), allowing compounds to be ranked and prioritized. Finally, it also enables the identification of the mode of inhibition. We show kinCSM performs as well as or better than similar methods and can generate biological insights into what makes potent CDK2 inhibitors.We have previously shown that the concept of graph‐based signatures could be used to model both protein and small molecule structures,22.1i (Pearson's correlation coefficient of up to 0.21). Across our datasets, both CDK2 inhibitors (IC50 < 10 μM) and non‐inhibitors (IC50 ≥ 10 μM) generally followed Lipinski's rule of five (RO5)p) <5, no more than 10 rotatable bonds, and TPSA <140 Å2 , indicating they are more hydrophilic and are more likely to be distributed in aqueous regions such as blood serum. Consistent with this observation, inhibitors also had a larger TPSA  compared to non‐inhibitors, reflecting a potential to establish more interactions with CDK2.Despite a modest correlation between inhibition strength and drug‐likeness properties, some physicochemical properties did distinguish between CDK2 inhibitors and non‐inhibitors. Potent CDK2 inhibitors had a lower log 2.2sulfanilamide (16.2% support) and 2‐(N‐Anilino)pyrimidine (10.1% support) that occurred more frequently in CDK2 inhibitors compared to non‐inhibitors ,Few studies have been devoted to exclusively annotate different types of available CDK2 inhibitors. While the limited binding mode information on CDK2 inhibitors does not allow us to search for enriched fragments and build a dedicated model on classifying different types of CDK2 inhibitors, the consistent binding modes of the same molecule with different kinases enabled us to utilize the information from other kinase–ligand structures, which can also be applied to CDK2 inhibitors. The enriched substructure (24.2% support) in Type II inhibitors is composed of a 1‐Phenylurea connected to a ring Figure . The oddSubstructure enrichment for Type I, I1/2, and allosteric inhibitors was not thoroughly analyzed. Type I and I1/2 inhibitors form stronger interactions with the hinge region similar to ATP, without having access to the back pocket. As all of Type I, I1/2, and Type II inhibitors share common substructures capable of occupying the ATP binding site, no substructure was found exclusively in Type I and I1/2 inhibitors. Additionally, the limited sample size for allosteric inhibitors  did not allow for unbiased enrichment analysis.2.3Our predictive model was trained using different supervised learning algorithms. The best performing algorithm, Extra Tree Classifier (M5P) with 23 features (identified via feature selection), was chosen. Table p‐values. Inhibitors tend to have higher partial charges and van der Waals surface area contributions (PEOE_VSA12 attribute), a higher frequency of sulfonamides, and more hydrogen bond donors . These characteristics reveal different non‐covalent interactions between enriched substructures (sulfanilamide and 2‐(N‐Anilino)pyrimidine) and CDK2, including electrostatic interactions, hydrogen bonds, and van der Waals forces, which can stabilize favorable inhibitor binding.To shed light on properties that can explain differences between CDK2 inhibitors and non‐inhibitors, we conducted a two‐sample Kolmogorov–Smirnov test on the feature set. Figure Compared to the deep learning models developed by Balachandar et al. on the same dataset, our classical machine learning algorithm has competitive performance. On the blind test, we achieved an AUC of 0.84, whereas Balachandar et al. achieved an AUC of 0.73.2.4i values of small molecules, the inhibition strength can be quantified. A Random Forest Regressor with 22 features was trained and validated. Table By predicting the pKi, which has a Spearman's correlation of around 0.8 based on 10,000 samplings of replicated pKi values. The performances obtained by kinCSM in predicting ligand‐CDK2 pKi achieved Spearman's correlations of 0.71 on cross‐validation, and 0.59 on the blind test, approaching the maximal performance we would see using independent experimental measurements.A recent studyi value of 6, we were able to compare the physicochemical differences between the defined potent inhibitors (pKi ≥ 6) and non‐inhibitors (pKi < 6) in cell‐based assays using the two‐sample Kolmogorov–Smirnov test. Figure p‐values <0.001) discriminating molecules with a high binding affinity using a more stringent threshold (pKi ≥ 6). These features were consistent with those identified previously in our classifier, where a lower threshold was used  as an initial crude screening. Moreover, the threshold pKi = 6 also led to the highest performance when testing classification by regression , highlighting the distinct differences between CDK2 inhibitors and non‐inhibitors under this threshold.By dividing the molecules into two groups with the cut‐off pKi cut‐off could also potentially be useful for classification purposes, in general, continuous labels can have higher variance compared to discrete classes, and may lead to poor classification performance. Instead, more information could be gained by considering the outputs from both classification and regression models. Accordingly, kinCSM provides a platform to quantify and rank inhibition strength in addition to inhibitor identification.While the regression model with the pKi ≥ 5.5 predicted by our regressor. Using the combined information, further validation results on active compounds and decoys  from DUD‐EAs a guideline to screen potent CDK2 inhibitors, we suggest users to take compounds, which meet the two following criterion (from crude to refined screening): (1) compounds identified as “inhibitor” by our classifier; (2) compounds with a moderately high pK2.5The dataset for the classification of inhibitor types is highly unbalanced , which significantly increases the challenges of identifying the minority classes. However, our model was able to distinguish Type II inhibitors from Type I1/2 inhibitors, despite their smaller sample sizes. As shown in Table Compared to the best machine learning model developed by Miljković et al.Another challenge for this task was to do with the molecular structures of the three ATP competitive inhibitor types, which can be modeled as a continuum instead of distinct categories as the kinase conformation they bind changes in a stepwise manner.p as shown in Figure t‐test p‐values <0.001 compared to Type I and II).Being positioned in the middle of the continuum, Type I1/2 becomes the most challenging class, even though it has an adequate sample size. The Type I versus Type I1/2 classifier achieved the lowest performance  as shown in Figure p‐values <0.001, Figure Although Type II inhibitors have a distinctive characteristic (back pocket access), a larger sample size still causes biased predictions toward Type I inhibitors Figure . The Typ2.6https://biosig.lab.uq.edu.au/kin_csm/. Users can identify CDK2 inhibitors, predict CDK2 pKi and possible binding modes by providing a single molecule or a list of molecules as SMILES strings , and classify different types of inhibitors in a single resource. This tool can be used to study both the binding affinity and favorable binding modes of CDK2 inhibitors.Here, we developed kinCSM, the first predictive tool to identify CDK2 inhibitors, predict CDK2 Ligand–Kinase Inhibition Constant  for inhibitor type classification. All datasets used in this study are available at https://biosig.lab.uq.edu.au/kin_csm/.Molecules with labels were curated from three different literature sourcesrdkit.ML.Cluster.Butina module in the cheminformatics toolkit RDKitTanimotoSimilarity.The training and test sets in the previous article were not low‐redundant, and will have led to data contamination and over‐estimated performance. To address this problem, in our work, the datasets were split into low‐redundancy training (70%) and blind test (30%). We ensured the molecules in the training and blind test sets have similar label distribution but are in different similarity clusters. The clusters were formed using the 4.2Molecular features for machine learning were extracted from SMILES strings as done previously.Acceptor, Donor, PosIonizable, NegIonizable, iAromatic, and Hydrophobe) as shown in the bottom left panel of Figure The graph‐based signatures are distance patterns that are generated iteratively by the Cutoff Scanning Matrix (CSM) algorithm.4.3Different machine learning models were trained and assessed under 10‐fold cross‐validation within the training set (70%). We then evaluated the trained models on the blind test set (30%) and compared the performance of the machine learning methods.Specifically, in this study, we have compared the performance of the following popular machine learning techniques using the python Scikit‐learn libraryr), mean squared error (MSE), and root mean squared error (RMSE) for regression.Finally, the model performance was further evaluated by different metrics. MCC, F1 score and AUC for classification, Pearson's correlation coefficient (r) for regression, to simplify models and reduce noise.A bottom‐up greedy feature selection method was used according to MCC for classification, and Pearson's Correlation Coefficient ; The National Health and Medical Research Council of Australia (GNT1174405 to D.B.A.), and The Victorian Government's Operational Infrastructure Support Program.Yunzhuo Zhou: Data curation (lead); formal analysis (lead); methodology (lead); writing (lead). Raghad Al‐Jarf: Data curation (supporting); methodology (supporting). Azadeh Alavi: Formal analysis (supporting). Thanh Binh Nguyen: Methodology (supporting); supervision (supporting). Carlos H. M. Rodrigues: Software (lead). Douglas E. V. Pires: Formal analysis (supporting); supervision (supporting). David B. Ascher: Conceptualization (lead); formal analysis (supporting); funding acquisition (lead); investigation (supporting); methodology (supporting); project administration (lead); software (supporting); supervision (lead); writing – review and editing (lead).The author declares that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.Table S1. Contingency table for a fragment  enriched in CDK2 inhibitors.Table S2. Model validation on a random subset of DUD‐E dataset based on the combined predictions from our classifier and regressor. The predicted inhibitors follow two conditions: (1) compounds identified as “inhibitor” by our classifier; (2) compounds with pKi ≥ 5.5 predicted by our regressor .Figure S1. Features generated by pkCSM. Two types of features are generated, including molecular properties (top right panel) and distance distribution between pharmacophore pairs (bottom left and bottom right panels). The example molecule is a type I1/2 CDK2 inhibitor, named RC‐3‐96 .Figure S2. Drug likeness property distributions for CDK2 inhibitors compared to non‐inhibitors. The violin plots show distributions of six physicochemical properties evaluating the drug likeness, including hydrogen acceptor and donor counts, log p, number of rotatable bonds, topological polar surface area (TPSA) and ring count. The white dot represents the median, and the range from the first quartile to the third quartile is shaded in black . We found both inhibitors  and non‐inhibitors  obey Lipinski's rule of five (RO5).Figure S3. Substructure enrichment in type II inhibitors. Compound 61 , a type II inhibitor of MAPK13. The enriched substructure  contains a urea connected to a benzene ring on one side, and an undefined ring on the other side. The benzene ring forms a sulfur‐ π and hydrophobic interactions with the gatekeeper residue Met107, and a ring interaction with Phe169 in the DFG motif. Meanwhile, the oxygen in the urea forms a hydrogen bond with Asp168 in the DFG, and the two nitrogen atoms form hydrogen bonds with GLu72, a conserved residue in αC‐helix (PDB code: 4eyj).Figure S4. Plots comparing CDK2 inhibitors and non‐inhibitors. (A) Inhibitors have higher PEOE_VSA12 , a higher frequency of sulfonamide and hydrogen bond donors compared to non‐inhibitors . (B) Molecules with higher binding affinity (pKi ≥ 6) are more likely to contain Pyrrole, they also have higher fr_Ar_N and PEOE_VSA12 .Figure S5. ROC curves for different binding mode classifiers. The ROC curves for type I versus II, type I versus I1/2, type I1/2 versus II and allosteric versus non‐allosteric are plotted. The type I1/2 versus II classifier has the highest Area Under the Curve, which means it can achieve higher performance by modifying the learned priors in our model.Figure S6. Physicochemical properties of different types of kinase inhibitors. Panels A (FCount) and B (Hydrophobe_Count) show the structural continuum of type I, I1/2 and II inhibitors, where type II has the highest fluorine and hydrophobe pharmacophores . Type I1/2 shows a similar distribution of MolLogP with type I (panel C), but higher fr_ArN (panel D) with p‐values <0.001 makes it distinguishable from other types. Type II inhibitors have a higher frequency of urea (panel E) with p‐values <0.001. In the bar plots, the ranges from the first quartile to the third quartile are shown in black lines.Figure S7. Integrated prediction performance on the blind test set. The prediction outcomes of binary classifiers were merged to enable multi‐class classification according to the majority vote.Click here for additional data file.