Pneumocystis jirovecii Pneumonia in Patients with Rheumatic Diseases: Risk Assessment and Prophylaxis

Pneumocystis jirovecii pneumonia (PJP) is an uncommon opportunistic infection in patients with rheumatic diseases with high mortality. Unlike other non-HIV conditions, international guideline for PJP prophylaxis in rheumatic diseases is currently lacking. Recent evidence regarding the risk of PJP and effectiveness of prophylaxis has been accumulating. This Review provides an update on the information about risk factors associated with PJP in patients with rheumatic diseases based on rheumatic diagnoses, use of immunosuppressive agents and other disease-related factors. The second part of the article summarizes evidence regarding the effectiveness of PJP prophylaxis by considering both disease-related and therapy-related factors. Finally, the Review outlined the currently available disease-specific recommendations and local guidelines, and appreciate the factors that influence physicians’ decision.

Designing Disease-Specific mHealth Apps for Clinical Value

mHealth apps for patient use are promising but continue to face a plateau in usage. Current apps work for a limited segment of the patient population, i.e., those who enjoy tracking for intrinsic rewards. There are many opportunities to support patient care in between health care provider visits that are not currently being met for many diseases and patient types (personas). This is an area of great potential growth for mHealth apps and could contribute greatly to patient health and wellness. In this chapter, we propose a framework for how to think about the between-visit needs of patients that would motivate continued use of mhealth apps. We view the app design process from the following perspectives: 1) disease-specific needs, 2) non-disease specific needs, 3) behavioral theoretical aspects of app usage and 4) app-intrinsic usage motivators. Myasthenia gravis serves as the use case for illustrating these perspectives and how to use them in designing a disease-specific mHealth app.

Parental Disease Specific Knowledge and Its Impact on Health-Related Quality of Life

Objective: Structured education programs have been shown to improve somatic outcome and health-related quality of life (HRQOL) in a variety of chronic childhood diseases. Similar data are scarce in paediatric liver transplantation (pLTx). The purpose of this study was to examine the relationship of parental disease-specific knowledge and psychosocial disease outcome in patients after pLTx. Methods: Parents of 113 children (chronic liver disease n = 25, after pLTx n = 88) completed the transplant module of the HRQOL questionnaire PedsQL, the “Ulm quality of life inventory for parents of children with chronic diseases” ULQUI, and a tailor-made questionnaire to test disease-specific knowledge. Results: Parental knowledge was highest on the topic of “liver transplantation” and lowest in “basic background knowledge” (76% and 56% correct answers respectively). Knowledge performance was only marginally associated with HRQOL scores, with better knowledge being related to worse HRQOL outcomes. In contrast, self-estimation of knowledge performance showed significant positive correlations with both PedsQL and ULQUI results. Conclusion: Patient HRQOL and parental emotional wellbeing after pLTx are associated with positive self-estimation of parental disease-specific knowledge. Objective disease-specific knowledge has little impact on HRQOL. Parental education programs need to overcome language barriers and address self-efficacy in order to improve HRQOL after pLTx.

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis

Background Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. Results We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Conclusion Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Base Editing of Human Pluripotent Stem Cells for Modeling Long QT Syndrome

Human pluripotent stem cells (hPSCs) have great potential for disease modeling, drug discovery, and regenerative medicine as they can differentiate into many different functional cell types via directed differentiation. However, the application of disease modeling is limited due to a time-consuming and labor-intensive process of introducing known pathogenic mutations into hPSCs. Base editing is a newly developed technology that enables the facile introduction of point mutations into specific loci within the genome of living cells without unwanted genome injured. We describe an optimized stepwise protocol to introduce disease-specific mutations of long QT syndrome (LQTs) into hPSCs. We highlight technical issues, especially those associated with introducing a point mutation to obtain isogenic hPSCs without inserting any resistance cassette and reproducible cardiomyocyte differentiation. Based on the protocol, we succeeded in getting hPSCs carrying LQTs pathogenic mutation with excellent efficiency (31.7% of heterozygous clones, 9.1% of homozygous clones) in less than 20 days. In addition, we also provide protocols to analyze electrophysiological of hPSC-derived cardiomyocytes using multi-electrode arrays. This protocol is also applicable to introduce other disease-specific mutations into hPSCs. Graphical abstract

Genome-wide association study identifies tumor anatomical site-specific risk variants for colorectal cancer survival

Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10–8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30–1.69, p = 8.47 × 10–9) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65–2.77, p = 9.19 × 10–9 and rs144717887, HR = 2.01, 95% CI 1.57–2.58, p = 3.14 × 10–8), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.

Oncologic Fidelity of Minimally Invasive Surgery to Resect Neoadjuvant-Treated Wilms Tumors

Background The Children's Oncology Group recommends upfront resection of Wilms tumor (WT), however, unique scenarios warrant neoadjuvant chemotherapy and delayed resection. We hypothesized that in the context of neoadjuvant chemotherapy, minimally invasive surgery (MIS) to resect WT achieves equivalent oncologic fidelity and better maintains therapy schedules. Methods A retrospective analysis of WT treated between 2010-2021 at a free-standing children's hospital was performed. Patient and disease specific characteristics were collected, and pre-resection tumor volumes (TV) were calculated. Impact of MIS or open resection on oncologic fidelity and time to resume chemotherapy was analyzed. Results For the study period, 62 patients were treated for 65 WT, and 14 patients (22.6%) received neoadjuvant chemotherapy to treat 17 WT (26.2%): 7 Stage I (all predisposition syndromes), 2 stage III, 7 stage IV, and 1 stage V (bilateral). MIS was utilized to resect 6 WT from 5 patients. For partial nephrectomy, pre-resection TV was 0.38 ml if MIS and 10.38 ml if open ( P = .025). For radical nephrectomy, pre-resection TV was 31.58 ml if MIS and 175.00 ml if open ( P = .101). No significant differences between surgical approach were detected regarding pathologic variables or survival. Epidural use was significantly greater with open procedures ( P = .001). Length of stay was 2.00 days after MIS compared to 6.00 for open resection ( P = .004). Time to resume chemotherapy was 7.00 days after MIS versus 27.00 for open ( P = .004). Conclusion After neoadjuvant chemotherapy for WT, MIS partial and radical nephrectomies achieved equivalent oncologic fidelity, reduced epidural use and post-operative stays, and better maintained adjuvant therapy timelines when compared to open resections.