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Compound volvulus or ileosigmoid knotting is an uncommon surgical emergency that causes intestinal obstruction. The sigmoid and ileum are mostly involved in this closed-loop intestinal obstruction. It is regarded as a rather uncommon cause of intestinal obstruction. It's important to distinguish between an ileosigmoid knot and a simple sigmoid volvulus since the management of the two is different. CT and MRI are more helpful in the diagnosis than abdominal X-ray findings, which are not pathognomonic. After resuscitation, a patient with ileosigmoid knotting typically needs an emergency laparotomy. Different resectional and non-resectional surgical procedures may be employed depending on the viability of the ileum and sigmoid colon.
RESUMO
BACKGROUND: Splenic injuries are common solid organ injuries resulting from blunt abdominal trauma in road traffic accidents. Very often, splenic injuries can be life-threatening. Earlier, splenic injuries were often dealt with surgical intervention, such as splenectomy. With the recognition of the immunological function of the spleen and possible complications of splenectomy surgery, such as overwhelming post-splenectomy infections (OPSI), there has been a recent trend for non-operative management (NOM). OBJECTIVE: To study the variables predicting failure of NOM in blunt abdominal trauma patients with splenic injury. METHODS: This is a retrospective study that includes 235 patients who presented to the Safdarjung Hospital emergency room (New Delhi, India) with blunt trauma abdomen and splenic injuries with or without associated injuries between January 2019 and December 2021. The data was entered in a Microsoft Excel spreadsheet (Microsoft Corp., Redmond, WA, USA). Categorical variables were expressed as frequencies and percentages. Pearson's chi-square test of association was used to determine if there is a relationship between two variables. A p-value of <0.05 was considered statistically significant. RESULTS: Out of 235 patients with blunt abdominal trauma and splenic injuries, 82 were hemodynamically unstable despite resuscitation and were taken up for emergency laparotomy. The remaining 153 patients, who were either hemodynamically stable or stabilized after adequate resuscitation, were managed on the lines of NOM. The number of patients with splenic injury in AAST grades 1, 2, 3, 4, and 5 was 36, 50, 40, 24, and three, respectively. Out of 153 patients, 130 (85%) were successfully managed by NOM, while eight (5%) had to discontinue NOM as they required surgical intervention. The failure of NOM (fNOM) is seen mostly with grade 5 injuries (2/2, 100%, p<0.01), followed by grade 4 (4/20, 20%) and grade 3 (2/37, 5.7%). The mean age in fNOM was 58.3 years, as compared to 42.2 years in the success of NOM (sNOM). All eight patients had multiple concomitant injuries, with femur fracture being the most common association in up to six patients (p<0.01), followed by liver injury in four patients. There were 15 mortalities, irrespective of AAST severity grade. All of these patients had associated concomitant injuries, with intracranial bleeding (n = 10, 32%, p<0.01) being the most common association, followed by femur fracture (n = 6, 20%) and liver injury (n = 5, 16%). Also, the cause of death was unrelated to splenic trauma (p = 0.67), with pulmonary embolism (n = 6, 40%, p<0.01) being the most common cause, followed by brain stem herniation (n = 5, 34%). CONCLUSION: Non-operative management is a safe and efficient method for treating patients with splenic injuries who are hemodynamically stable or stabilized. The factors associated with fNOM include elderly age, a higher American Association for the Surgery of Trauma (AAST) grade of splenic injury, and associated concomitant injuries. Femur fracture was the most common concomitant injury present in cases where NOM failed, followed by liver injury. The presence of intracranial bleeds in these patients was a common association with mortality, irrespective of the grade of splenic injury.
RESUMO
Urachal adenocarcinoma is one of the rare and aggressive neoplasms that often presents at an advanced stage and has a poor prognosis. Urachal adenocarcinoma makes up 0.17 to 0.34% of all bladder carcinomas. Patients commonly present with hematuria. Wide local excision of urachal mass with umbilicus and surrounding soft tissue en bloc combined with partial or radical cystectomy and bilateral pelvic lymphadenectomy is considered to be the primary surgical management. However, many publications in literature report that en bloc removal of tumor with umbilicus, entire urachal ligament, and bladder dome alone has long-term survival and disease-free period. Here, we present a case of a 50-year-old post-menopausal female patient with a fungating mass in the umbilical region of size approximately 10 cm in maximum diameter with mucopurulent discharge from the mass. The patient had no history of any hematuria, mucinuria, burning micturition, or any particles in urine. A laparotomy was performed, and tumor mass along with the cuff of the bladder dome was removed as en bloc along with umbilectomy. This case report highlights a rare case of urachal adenocarcinoma with a fungating mass of large size in a female patient which is otherwise more commonly seen in males. A review of published literature is also presented.
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Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
