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Nontyphoidal is a common cause of gastroenteritis but can also lead to bacteremia and extraintestinal infections, including meningitis (more frequent in children and infants), endovascular infections (e.g., endocarditis and infected aneurysms), urinary tract infections, and bone or bone marrow infections (e.g., septic arthritis and osteomyelitis). However, ENT complications are rare. We present the first-ever case of Enteritidis sinusitis. A 77-year-old woman experienced worsening right facial swelling and pain persisting for one month. Upon examination, she exhibited right cheek swelling with induration, warmth, and redness extending to the infraorbital region. Computed tomography (CT) scan findings revealed a heterogeneous mass in the right maxillary sinus with evidence of locoregional destruction. Additionally, an abscess was detected in the right buccal space. During surgery, the right maxillary sinus was found to contain pink frond-like tissue and white-grey concretions. Histological examination revealed squamous cell carcinoma (SCC). Magnetic resonance imaging (MRI) showed enlarged right cervical lymph nodes, raising suspicion for metastatic nodal spread. Further investigation indicated the presence of serovar Enteritidis in tissue cultures. The patient was ultimately diagnosed with stage IVA cT3N2bM0 right maxillary sinus squamous cell carcinoma (SCC) with concomitant Enteritidis sinusitis. Prior to the first surgery, she received treatment with amoxicillin-clavulanate for eight days, followed by six weeks of ciprofloxacin (culture-directed treatment) and two weeks of metronidazole to cover for anaerobes. Subsequently, she underwent a total maxillectomy, neck dissection and reconstruction utilizing a free anterolateral thigh flap, and adjuvant radiotherapy and is recovering well. We discuss the possible mechanism of Enteritidis infection in relation to kombucha intake.

Extracranial manifestation of arteriovenous malformations (AVMs) is uncommon. Nasoseptal AVMs are an even rarer entity. In this case report, we present an interesting and first-of-its-kind case of the development of a left nasoseptal AVM in a 60-year-old man after a fall. This was likely post-traumatic, unlike the usual congenital AVMs described in the literature. The patient was managed conservatively with regular follow-up for the AVM as he was asymptomatic.

A 28-year-old female domestic helper presented to the Ear, Nose, and Throat clinic complaining of three weeks of right otalgia associated with a right blocked ear. The hearing was otherwise normal, and she denied otorrhoea, dizziness or imbalance, ear digging, or water contact, and has no history of ear eczema. She has no other past medical history and no recent travel history. On examination, numerous whitish ovoid lesions were seen lining the entire right external auditory canal (EAC), admixed with debris, and the canal was inflamed. Her tympanic membrane was intact. The contralateral ear was normal. She was diagnosed with otoacariasis. The ear mites were unable to be retrieved for a direct microscopic examination, but based on the ovoid, smooth, and translucent morphology of the mites, they likely belong to the Acaridae or Chortoglyphidae families of mites. She underwent aural toileting for complete removal of the ear mites and was prescribed ear drops containing dexamethasone and polymyxin B sulfate for 2 weeks. She was reviewed using the otomicroscopy technique two weeks later and had made an uneventful recovery with no recurrence of ear mites.

Despite the promise of foundation models in medical AI, current systems remain limited - they are modality-specific and lack transparent reasoning processes, hindering clinical adoption. To address this gap, we present EVLF-FM, a multimodal vision-language foundation model (VLM) designed to unify broad diagnostic capability with fine-grain explainability. The development and testing of EVLF-FM encompassed over 1.3 million total samples from 23 global datasets across eleven imaging modalities related to six clinical specialties: dermatology, hepatology, ophthalmology, pathology, pulmonology, and radiology. External validation employed 8,884 independent test samples from 10 additional datasets across five imaging modalities. Technically, EVLF-FM is developed to assist with multiple disease diagnosis and visual question answering with pixel-level visual grounding and reasoning capabilities. In internal validation for disease diagnostics, EVLF-FM achieved the highest average accuracy (0.858) and F1-score (0.797), outperforming leading generalist and specialist models. In medical visual grounding, EVLF-FM also achieved stellar performance across nine modalities with average mIOU of 0.743 and Acc@0.5 of 0.837. External validations further confirmed strong zero-shot and few-shot performance, with competitive F1-scores despite a smaller model size. Through a hybrid training strategy combining supervised and visual reinforcement fine-tuning, EVLF-FM not only achieves state-of-the-art accuracy but also exhibits step-by-step reasoning, aligning outputs with visual evidence. EVLF-FM is an early multi-disease VLM model with explainability and reasoning capabilities that could advance adoption of and trust in foundation models for real-world clinical deployment.