Machine learning modeling to predict causes of infectious abortions and perinatal mortalities in cattle.

A plethora of infectious and non-infectious causes of bovine abortions and perinatal mortalities (APM) have been reported in literature. However, due to financial limitations or a potential zoonotic impact, many laboratories only offer a standard analytical panel, limited to a preestablished number of pathogens. To improve the cost-efficiency of laboratory diagnostics, it could be beneficial to design a targeted analytical approach for APM cases, based on maternal and environmental characteristics associated with the prevalence of specific abortifacient pathogens. The objective of this retrospective observational study was to implement a machine learning pipeline (MLP) to predict maternal and environmental factors associated with infectious APM. Our MLP based on a greedy ensemble approach incorporated a standard tuning grid of four models, applied on a dataset of 1590 APM cases with a positive diagnosis that was achieved by analyzing an extensive set of abortifacient pathogens. Production type (dairy/beef), gestation length, and season were successfully predicted by the greedy ensemble, with a modest prediction capacity which ranged between 63 and 73 %. Besides the predictive accuracy of individual variables, our MLP hierarchically identified predictor importance causes of associated environmental/maternal characteristics of APM. For instance, in APM cases that happened in beef cows, season at APM (spring/summer) was the most important predictor with a relative importance of 24 %. Furthermore, at the last trimester of gestation Trueperella pyogenes and Neospora caninum were the most important predictors of APM with a relative importance of 22 and 17 %, respectively. Interestingly, herd size came out as the most relevant predictor for APM in multiparous dams, with a relative importance of 12 %. Based on these and other mix of predicted environmental/maternal and pathogenic potential causes, it could be concluded that implementing our MLP may be beneficial to design a more cost-effective, case-specific diagnostic approach for bovine APM cases at the diagnostic laboratory level.

Development of diagnostic algorithm for Cushing's syndrome: a tertiary centre experience.

PURPOSE: No consensus exists as the gold standard for Cushing's Syndrome (CS) screening. This study aimed to evaluate the diagnostic accuracy and utility of late-night salivary cortisol (LNSC) and cortisone (LNSE), overnight dexamethasone suppression test (ODST), and urinary free cortisol (UFC) in developing a screening algorithm for CS. METHODS: A retrospective, single-centre analysis on 93 adult patients referred to the Oxford Centre for Diabetes, Endocrinology, and Metabolism for CS evaluation (2017-2022). Data were analysed using binomial logistic regression and area under the receiver-operating curve (AUROC). RESULTS: Fifty-three patients were diagnosed with CS. LNSC (sensitivity 87.5%, specificity 64.9%, AUC 0.76), LNSE (sensitivity 72.4%, specificity 85.7%, AUC 0.79), and ODST (sensitivity 94.7%, specificity 52.1%; AUC 0.74) demonstrated comparable effectiveness for CS diagnosis. Their combined application increased diagnostic accuracy (AUC 0.91). UFC was not statistically significant. Pre-test clinical symptom inclusion improved screening test performance (AUC LNSC: 0.83; LNSE: 0.84; ODST: 0.82). For CD diagnosis, LNSE + LNSC (AUC 0.95) outperformed ODST. Combining these with ACTH levels < 12.6 pmol/L perfectly distinguished MACS (AUC 1.00). ODST (AUC 0.76) exhibited superior performance (sensitivity 100.0%, specificity 52.2%) in MACS detection. CONCLUSIONS: LNSC, LNSE, and ODST are robust tools for CS screening, with their combined use offering the highest diagnostic precision. LNSE, especially when used with LNSC, is highly effective for CD diagnosis, exceeding ODST accuracy. ODST is preferable for MACS identification. Integrating ACTH levels markedly improves differentiation between CD and MACS. Conversely, UFC shows limited diagnostic utility.

Enhancing bovine abortion surveillance: A learning experience.

Abortions and perinatal mortalities (APM) substantially affect cattle industry efficiency. Various infectious and noninfectious factors have been associated with bovine APM worldwide. Infections are often considered pivotal due to their abortifacient potential, leading laboratories to primarily investigate relevant infectious agents for APM cases. Some infectious causes, such as Brucella abortus, have also a zoonotic impact, necessitating monitoring for both animal and human health. However, underreporting of bovine APM is a global issue, affecting early detection of infectious and zoonotic causes. Previous studies identified factors influencing case submission, but regional characteristics may affect results. In Belgium, farmers are obliged to report cases of APM within the context of a national brucellosis monitoring program. The inclusion criteria for this monitoring program cover abortions (gestation length of 42-260 d) and perinatal mortalities of (pre)mature calves following a gestation length of more than 260 d, which were stillborn or died within 48 h after birth. The objective of the present study was to describe the evolution in submission of APM cases within a mandatory abortion monitoring program in relation to subsidized initiatives in the northern part of Belgium. Based on the proportion of APM submissions versus the proportion of bovine reproductive females, an APM proportion (APMPR) was calculated, and factors at both animal and herd level that may influence this APMPR were explored by using linear models. This evaluation revealed that the APMPR increased with the introduction of an extensive analytical panel of abortifacient agents and a free on-farm sample collection from 0.44% to 0.94%. Additionally, an increase of the APMPR was associated with an outbreak of an emerging abortifacient pathogen (Schmallenberg virus; 1.23%), and the introduction of a mandatory eradication program for bovine viral diarrhea virus (BVDv; 1.20%). The APMPR was higher in beef compared with dairy cattle, and it was higher in winter compared with fall, spring, and summer. Smaller herds categorized in the first quartile had a higher APMPR compared with larger herds. Herds that submitted an APM in the previous year had a higher APMPR in the next year compared with herds without an APM submission. Finally, herds for which there was evidence of the presence of BVDv had a higher APMPR compared with herds without evidence of the presence of BVDv. In conclusion, the number of APM submissions increased after the introduction of a free on-farm sample collection and an extensive pathogen screening panel. Production type (beef), season (winter), smaller herd size, previous APM, and presence of BVDv seemed to have a positive effect on APMPR. However, even under mandatory circumstances, APM still seems to be underreported, since the APMPR was lower than the expected minimal rate of 2%. Therefore, further research is necessary to identify the drivers that convince farmers to submit APM cases to improve submission rates and ensure an efficient monitoring program for APM and eventually associated zoonotic pathogens.

Gut feelings: the relations between depression, anxiety, psychotropic drugs and the gut microbiome.

The gut microbiome is involved in the bi-directional relationship of the gut - brain axis. As most studies of this relationship are small and do not account for use of psychotropic drugs (PTDs), we explored the relations of the gut microbiome with several internalizing disorders, while adjusting for PTDs and other relevant medications, in 7,656 Lifelines participants from the Northern Netherlands (5,522 controls and 491 participants with at least one internalizing disorder). Disorders included dysthymia, major depressive disorder (MDD), any depressive disorder (AnyDep: dysthymia or MDD), generalized anxiety disorder (GAD) and any anxiety disorder (AnyAnx: GAD, social phobia and panic disorder). Compared to controls, 17 species were associated with depressive disorders and 3 were associated with anxiety disorders. Around 90% of these associations remained significant (FDR <0.05) after adjustment for PTD use, suggesting that the disorders, not PTD use, drove these associations. Negative associations were observed for the butyrate-producing bacteria Ruminococcus bromii in participants with AnyDep and for Bifidobacterium bifidum in AnyAnx participants, along with many others. Tryptophan and glutamate synthesis modules and the 3,4-Dihydroxyphenylacetic acid synthesis module (related to dopamine metabolism) were negatively associated with MDD and/or dysthymia. After additional adjustment for functional gastrointestinal disorders and irritable bowel syndrome, these relations remained either statistically (FDR <0.05) or nominally (P < 0.05) significant. Overall, multiple bacterial species and functional modules were associated with internalizing disorders, including gut - brain relevant components, while associations to PTD use were moderate. These findings suggest that internalizing disorders rather than PTDs are associated with gut microbiome differences relative to controls.

[Euthanasia requests by patients with a borderline personality organisation]. / Euthanasieverzoek door een patiënt met een borderlinepersoonlijkheidsorganisatie.

Psychodynamic processes may play a role in the evaluation of a euthanasia request from a patient with a borderline personality organization. From the perspective of two-person psychology it is understandable that, unbearable and irremediable suffering (important conditions for euthanasia) are not only characteristics of the patients’ suffering, but also acquire meaning in the interaction with the psychiatrist. It is important that the psychiatrist recognizes immature defence mechanisms and forms of non-mentalizing. Understanding how symptoms of personality pathology may become manifest in the therapeutic relationship can be helpful in the dialogue with the patient about the potential impact of personality dynamics on the request for euthanasia and treatment options.

Unaffected semen quality parameters in Neospora caninum seropositive Belgian Blue bulls.

Neospora caninum is a protozoan parasite that causes abortion, perinatal mortality, and subfertility in cattle worldwide. Despite the presence of the DNA of the parasite in semen of infected bulls, the effect on semen quality has not been extensively studied. This study aimed to investigate the effect of a natural Neospora caninum infection on fresh and frozen semen quality parameters in Belgian Blue bulls. Two hundred and fourteen bulls were serologically screened with an indirect ELISA-test specific for anti-Neospora caninum antibodies, every two months during one year. In addition to serological screening, semen was collected twice weekly using an artificial vagina. The following semen quality parameters were assessed: ejaculate volume, concentration, total motility of fresh semen samples, as well as morphology, total and progressive motility for frozen/thawed semen samples. Bulls were semen sampled throughout the whole year, but only semen samples of bulls that had six consecutive positive or negative ELISA-test results were included in our dataset (n = 98). Generalized linear and binomial mixed models were used for statistical analysis of each outcome variable. In these models the explanatory variables were defined as: age, barn location, mean Temperature Humidity Index (THI) during sperm production (14-42 days before sampling), maximum daily THI at collection, season of sperm production, season at collection and the Neospora caninum antibody test results. Initially, individual explanatory variables were tested in univariable models for each outcome variable. Akaike information criterion (AIC) values were used to select explanatory variables to build a multivariable model, where the Neospora caninum test result was forced in all models. The present study reveals an overall apparent seroprevalence of Neospora caninum of 9,2% in the study population. No significant associations were detected between natural neosporosis, substantiated by ELISA-antibody levels, and any of our tested outcome variables on fresh and frozen/thawed semen samples. Based on the results of the present study, we conclude that Neospora caninum seropositive bulls do not have lower semen quality parameters compared with seronegative bulls.

Retrospective study of factors associated with bovine infectious abortion and perinatal mortality.

Abortion and perinatal mortality, leading causes of economic loss in cattle industry, are the consequence of both non-infectious and a wide range of infectious causes. However, the relative contribution of pathogens to bovine abortion and perinatal mortality is poorly documented, since available studies involved only a limited number of pathogens. Therefore, the objectives of the present monitoring study were to determine the prevalence of infectious agents associated with bovine abortion and perinatal mortality, and to identify differences in production type, gestation length, parity and seasonality by using mixed effect models (logistic regression). A pre-established sampling protocol based on the collection of the aborted fetus/calf and a corresponding maternal blood sample, involving diagnostic testing for 10 pathogens, was performed. At least one potential causal agent of the abortion or perinatal mortality was detected in 39 % of cases. In these diagnosed cases, Neospora caninum was the most detected pathogen, followed by Trueperella pyogenes, BVDv, Escherichia coli, and Aspergillus fumigatus. Neospora caninum [odds ratio (OR): 0.4; 95 % confidence interval (CI): 0.3-0.7] and Aspergillus fumigatus (OR: 0.1; 95 % CI: 0.1-0.3) were detected less in late versus early gestation. Aspergillus fumigatus was less common in dairy in comparison to beef abortion cases (OR: 0.2; 95 % CI: 0.1-0.6). Winter was associated with a lower positivity for Neospora caninum and BVDv in comparison to warmer seasons. Despite extensive diagnostic testing, an etiological diagnosis was not reached in 61 % of cases, highlighting the need for even more extensive (non-)infectious disease testing or more accurate tests.

Temperature-resilient solid-state organic artificial synapses for neuromorphic computing.

Devices with tunable resistance are highly sought after for neuromorphic computing. Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance tuning and excessive write noise, degrading artificial neural network (ANN) accelerator performance. Emerging electrochemical random-access memories (ECRAMs) display write linearity, which enables substantially faster ANN training by array programing in parallel. However, state-of-the-art ECRAMs have not yet demonstrated stable and efficient operation at temperatures required for packaged electronic devices (~90°C). Here, we show that (semi)conducting polymers combined with ion gel electrolyte films enable solid-state ECRAMs with stable and nearly temperature-independent operation up to 90°C. These ECRAMs show linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, submicrosecond write-read cycling, low noise, and low-voltage (±1 volt) and low-energy (~80 femtojoules per write) operation combined with excellent endurance (>109 write-read operations at 90°C). Demonstration of these high-performance ECRAMs is a fundamental step toward their implementation in hardware ANNs.

Cardiac arrhythmia triggered by diuretic-induced hyponatremia.

Diuretics have a long and distinguished history in the treatment of hypertension and heart failure. Clinical practice guidelines recommend that diuretics should be considered to be as suitable as other antihypertensive agents for the initiation and maintenance of antihypertensive treatment. However, diuretics may potentially cause electrolyte disturbances and metabolic side effects. Diuretic-induced hyponatremia is probably more prevalent than generally acknowledged. We present an unusual case of indapamide-induced hyponatremia and hypokalemia complicated by cardiac arrhythmia. The adverse drug reaction was reversible and non-life-threatening, but this case serves as a reminder that careful evaluation and constant monitoring are necessary when prescribing diuretics.

Anxiety and depression in patients with haematological neoplasms in Malaysia.

BACKGROUND: It is not uncommon that anxiety and depression occur in patients with cancers, and past researches have shown that the quality of life of patients is negatively affected. This study aims to determine the prevalence of anxiety and depression of patients with haematological cancers in Malaysia and to investigate the possible association of these psychological symptoms with their quality of life. METHODS: This is a cross-sectional study where patients with haematological cancers attending two major hospitals were recruited. Anxiety and depression symptoms were assessed using the Hospital Anxiety and Depression scale (HADS). Quality of life (QoL) of these patients was measured using the European Organisation for Research and Treatment of Cancer quality of life questionnaire (EORTC QLQ C30). An overall summary QoL score in combination with financial difficulty score and global health score were used for analysis. RESULTS: A total of 319 patients were recruited. Thirty-three percent of patients had anxiety symptoms, 23.5% had depression symptoms. In summary the overall score of QoL is significantly lower in patients with higher scores for depression and anxiety, (p<0.05). Patients who exhibit anxiety symptoms were more frequently female, still undergoing treatment whereas patients who had higher depression scores were older and had acute leukemias or myeloproliferative neoplasms. Patients who have depression are significantly associated with a higher financial difficulty score, p<0.05. CONCLUSION: The poor quality of life in patients who have anxiety and depression should raise awareness amongst the health professions treating them so that additional support can be provided.

Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk.

BACKGROUND: Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD? METHODS: A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests. RESULTS: Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60-0.71). LIMITATIONS: This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men. CONCLUSIONS: Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women.

Schmallenberg Virus in Belgium: Estimation of Impact in Cattle and Sheep Herds.

Schmallenberg virus (SBV) emerged during summer 2011. SBV induced an unspecific syndrome in cattle and congenital signs (abortions, stillbirths and malformations) in domestic ruminants. To study the impact of SBV in Belgium, a phone survey was conducted upon September 2012. Hereto two groups of cattle farmers (A and B) and two groups of sheep farmers (C and D) were randomly selected. Farms from groups A (n = 53) and C (n = 42) received SBV-positive result at RT-PCR in the Belgian National Reference Laboratory (NRL). Farms from groups B (n = 29) and D (n = 44) never sent suspected samples to NRL for SBV analysis but were however presumed seropositive for SBV after the survey. Questionnaires related to reproduction parameters and clinical signs observed in newborn and adult animals were designed and addressed to farmers. As calculated on a basis of farmers' observations, 4% of calves in group A and 0.5% in group B were reported aborted, stillborn or deformed due to SBV in 2011-2012. The impact as observed by sheep farmers was substantially higher with 19% of lambs in group C and 11% in group D that were reported aborted, stillborn or deformed due to SBV in 2011-2012. Interestingly, abortions or stillbirths were not clear consequences of SBV outbreak in cattle farms, and the birth of a deformed animal was an essential condition to suspect SBV presence in cattle and sheep farms. This study contributes to a better knowledge of the impact of the SBV epidemic. The results suggest that SBV impacted Belgian herds mostly by the birth of deformed calves, stillborn lambs and deformed lambs. This work also demonstrates that the birth of a deformed calf or lamb was a trigger for the farmer to suspect the presence of SBV and send samples to NRL for further analyses.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Casting wider nets for anxiety and depression: disability-driven cross-diagnostic subtypes in a large cohort.

BACKGROUND: In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures. METHOD: A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables. RESULTS: The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms ['Somatic' (13.0%), and 'Worried' (14.0%)] and psychopathological symptoms ['Subclinical' (8.8%), and 'Clinical' (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1-12.3, and chronic stress: OR 3.7-4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found. CONCLUSIONS: An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.

A new predilection site of Mycoplasma bovis: Postsurgical seromas in beef cattle.

Mycoplasma bovis is a highly contagious bacterium, which predominantly causes chronic pneumonia, otitis and arthritis in calves and mastitis in adult cattle. In humans, Mycoplasma species have been associated with post-surgical infections. The present study aimed to identify the bacteria associated with three outbreaks of infected seromas after caesarian section in Belgian Blue beef cattle. A total of 10 cases occurred in three herds which were in close proximity of each other and shared the same veterinary practice. M. bovis could be cultured from seroma fluid in five of the six referred animals, mostly in pure culture and was isolated from multiple chronic sites of infection (arthritis and mastitis) as well. DNA fingerprinting of the isolates targeting two insertion sequence elements suggested spread of M. bovis from chronic sites of infection (udder and joints) to the postsurgical seromas. Identical genetic profiles were demonstrated in two animals from two separate farms, suggesting spread between farms. Mortality rate in the referred animals positive for M. bovis in a seroma was 80% (4/5), despite intensive treatment. A massive increase in antimicrobial use was observed in every affected farm. These observations demonstrate involvement of mycoplasmas in outbreaks of postsurgical seromas in cattle.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.