Influence of caseload and surgical speciality on outcome following surgery for colorectal cancer: a review of evidence Part 2: Long-term outcome

OBJECTIVE: We reviewed recent literature to assess the impact of hospital caseload, surgeon's caseload and education on long-term outcome following colorectal cancer surgery. METHOD: We searched the MEDLINE and Cochrane Library databases for relevant literature starting from 1992. We selected hospital caseload, surgeon's caseload and surgeon's education, type of hospital, and surgeon's experience as variables of interest. Measures of outcome were recurrence-free survival and overall survival, and for rectal cancer frequency of permanent stoma. We reviewed the 34 studies according to tumour location: colonic cancer, rectal cancer, or colorectal cancer. We described the studies individually and performed a meta-analysis whenever it was considered appropriate. RESULTS: For colonic cancer, overall survival improved with increasing hospital caseload, odds ratio (OR) 1.22 [95% confidence interval (CI) 1.16-1.28], and surgeon's education. For rectal cancer, overall survival improved with increasing hospital caseload, OR 1.38 (95% CI 1.19-1.60), and, possibly by surgeon' education and experience. Cancer-free survival was strongly influenced by surgeon's education. The colostomy rate was less in high caseload hospitals, OR 0.76 (95% CI 0.68-0.85). For colorectal cancer, overall survival improved with surgeon's education. CONCLUSION: The data have provided evidence that long-term survival following colorectal cancer surgery in general improved significantly with increasing hospital caseload and surgeon's education.     

Enteric pathogens and their isolation at a cancer hospital in Pakistan

Diarrhoeal diseases remain a major cause of mortality and morbidity in developing countries. However, due to lack of funds, supply problems and some inexperience, some laboratories have difficulty identifying a causative agent in stool samples. In the year following the opening of the Shaukat Khanum Memorial Cancer Hospital and Research Centre in Lahore, Pakistan, the microbiology department had not isolated a single enteric pathogen. From January 1996, new culture techniques were introduced, with a resulting increase (10%) in identification of these pathogens. In addition, the introduction of formol-ether concentration made a significant contribution to the number of intestinal parasites seen. This report demonstrates how simple microbiology methods made a difference to the running of the department and, ultimately, to the patients.     

Identification of a type 1 diabetes-associated CD4 promoter haplotype with high constitutive activity

CD4 is a candidate gene in autoimmune diseases, including Type 1 diabetes mellitus (T1DM), because the CD4 receptor is crucial for appropriate antigen responses of CD4(+) T cells. We previously found linkage between a CD4-1188(TTTTC)(5-14) promoter polymorphism and T1DM. In the present study, we screened the human CD4 promoter for mutations and identified three frequent single nucleotide polymorphisms (SNPs): CD4-181C/G, CD4-521C/G and CD4-1050T/C. The SNPs are in strong linkage disequilibrium (LD) and association with the CD4-1188(TTTTC)(5-14) alleles, and we observed nine CD4 promoter haplotypes, of which four are frequent. We genotyped the SNPs in 253 Danish T1DM families (1129 individuals) and found evidence for linkage and association of a CD4 (A4(-1188)T(-1050)G(-521)C(-181)) haplotype to T1DM. In reporter studies, we show that (1) the T1DM-associated CD4 haplotype encodes high constitutive promoter activity and (2) the CD4-181G variant encodes higher stimulated promoter activity than the CD4-181C variant. This difference is in part neutralized in the frequently occurring CD4 promoter haplotypes by the more upstream genetic variants. Thus, we report functional impact of a novel CD4-181C/G SNP on stimulated CD4 promoter activity and the identification of a novel CD4 haplotype with high constitutive promoter activity that is linked and associated with T1DM.     

Environmental conditions and variation in levels of sun exposure among children in child care

Sun exposure in childhood is 1 of the risk factors for developing skin cancer, yet little is known about levels of exposure at this age. This is particularly important in countries with high levels of ultraviolet radiation (UVR) such as Australia. Among 49 children 3 to 5 years of age attending child care centers, UVR exposure was studied under 4 conditions in a repeated measures design; sunny days, cloudy days, teacher’s instruction to stay in the shade, and a health professionals instruction to apply sunscreen. Three different data collection methods were employed: (a) completion of questionnaire or diary by parents and researcher, (b) polysulphone dosimeter readings, and (c) observational audits (video recording). Results of this study indicated that more than half the children had been sunburnt (pink or red) and more than a third had experienced painful sunburn (sore or tender) in the last summer. Most wore short sleeve shirts, short skirts or shorts and cap, that do not provide optimal levels of skin protection. However, sunscreen was applied to all exposed parts before the children went out to the playground. Over the period of 1 hr (9–10 a.m.) the average amount of time children spent in full sun was 22 min. On sunny days there was more variation across children in the amount of sun exposure received. While the potential amount of UVR exposure for young children during the hour they were outside on a sunny day was 1.45 MED (Minimum Erythemal Dose), they received on average 0.35 MED, which is an insufficient amount to result in an erythemal response on fair skin even without the use of sunscreen.     

Three months of COVID‐19: A systematic review and meta‐analysis

The pandemic of 2019 novel coronavirus (SARS‐CoV‐2019), reminiscent of the 2002‐SARS‐CoV outbreak, has completely isolated countries, disrupted health systems and partially paralyzed international trade and travel. In order to be better equipped to anticipate transmission of this virus to new regions, it is imperative to track the progress of the virus over time. This review analyses information on progression of the pandemic in the past 3 months and systematically discusses the characteristics of SARS‐CoV‐2019 virus including its epidemiologic, pathophysiologic, and clinical manifestations. Furthermore, the review also encompasses some recently proposed conceptual models that estimate the spread of this disease based on the basic reproductive number for better prevention and control procedures. Finally, we shed light on how the virus has endangered the global economy, impacting it both from the supply and demand side.     

Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images

Purpose: Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent.

Methods: A closed-form steady state model is trained on and then subsequently compared to N?=?20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57?°C isotherms of the thermometry data and the model-predicted ablation regions; 57?°C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (ω) and optical parameter (μ_eff) values, throughout a parameter space totalling 11?440 value-pairs. This represents a lookup table of μ_eff–ω pairs with the corresponding DSC value for each patient dataset. The μ_eff–ω pair with the maximum DSC calibrates the model parameters, maximising predictive value for each patient. Finally, leave-one-out cross-validation with global optimisation information trains the model on the entire clinical dataset, and compares against the model naïvely using literature values for ω and μ_eff.

Results: When using naïve literature values, the model’s mean DSC is 0.67 whereas the calibrated model produces 0.82 during cross-validation, an improvement of 0.15 in overlap with the patient data. The 95% confidence interval of the mean difference is 0.083–0.23 (p?Conclusions: During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive.