Bis(2-chloro-1,10-phenanthroline-κ2 N,N′)(thio­cyanato-κN)zinc (2-chloro-1,10-phenanthroline-κ2 N,N′)tris­(thio­cyanato-κN)zincate

The asymmetric unit of the title compound, [Zn(NCS)(C12H7ClN2)2][Zn(NCS)3(C12H7ClN2)], contains two cations and two anions. In the cations, the ZnII ions have distorted trigonal–bipyramidal environments formed by four N atoms from two 2-chloro-1,10-phenanthroline (cphen) ligands and one N atom from a thio­cyanate ligand. The ZnII atoms in the complex anions also have distorted trigonal–bipyramidal environments, formed by two N atoms from a cphen ligand and three N atoms from three thio­cyanato ligands. The crystal packing exhibits π–π inter­actions between the rings of the cphen ligands [shortest centroid–centroid distance = 3.586 (5) Å] and short inter­molecular S⋯Cl [3.395 (5) Å] and S⋯S [3.440 (4) Å] contacts

Quantum gates implementations in the separated ion-traps by fast laser pulses

An approach is proposed to implement the universal quantum gates between the ions confined individually in the separated traps. Instead of the typical adiabatic operations, performed for manipulating the ion-ion coupling, here the switchable couplings between ions are implemented non-adiabatically by using the fast laser pulses. Consequently, the desirable quantum gates between the ions could be implemented by using only a series of laser pulses. The proposal may be conveniently generalized to the quantum computation with the scalable ion-traps.Comment: 10 pages, 3figure

Aqua­[1-(1,10-phenanthrolin-2-yl-κ2 N,N′)-1H-pyrazol-3-amine-κN 2](sulfato-κO)copper(II) methanol monosolvate dihydrate

In the title compound, [Cu(SO4)(C15H11N5)(H2O)]·CH3OH·2H2O, the CuII ion is in a distorted square-pyramidal geometry, in which three N atoms from the chelating 1-(1,10-phenanthrolin-2-yl)-1H-pyrazol-3-amine ligand and one O atom from a sulfate anion define the basal plane and the O atom from the coordinating water mol­ecule is located at the apex. In the crystal, hydrogen-bonding inter­actions involving the coordinating and solvent water mol­ecules, the methanol solvent mol­ecule and the amine group (one with an intra­molecular inter­action to one of the sulfate O atoms) of the complex are observed. π–π inter­actions between symmetry-related phenantroline moieties, with a shortest centroid–centroid inter­action of 3.573 (2)°, are also present

Ammonium 2-(2,4-dichloro­phen­oxy)acetate hemihydrate

The title compound, NH4 +·C8H7Cl2O6 −·0.5H2O, was prepared by the reaction of 2-(2,4-dichloro­phen­oxy)­acetic acid and ammonia in water at 367 K. The mol­ecular structure and packing are stabilized by N—H⋯O and O—H⋯O inter­molecular hydrogen-bond inter­actions

Evaluating team-based, lecture-based, and hybrid learning methods for neurology clerkship in China: a method-comparison study

BACKGROUND: Neurology is complex, abstract, and difficult for students to learn. However, a good learning method for neurology clerkship training is required to help students quickly develop strong clinical thinking as well as problem-solving skills. Both the traditional lecture-based learning (LBL) and the relatively new team-based learning (TBL) methods have inherent strengths and weaknesses when applied to neurology clerkship education. However, the strengths of each method may complement the weaknesses of the other. Combining TBL with LBL may produce better learning outcomes than TBL or LBL alone. We propose a hybrid method (TBL + LBL) and designed an experiment to compare the learning outcomes with those of pure LBL and pure TBL. METHODS: One hundred twenty-seven fourth-year medical students attended a two-week neurology clerkship program organized by the Department of Neurology, Sun Yat-Sen Memorial Hospital. All of the students were from Grade 2007, Department of Clinical Medicine, Zhongshan School of Medicine, Sun Yat-Sen University. These students were assigned to one of three groups randomly: Group A (TBL + LBL, with 41 students), Group B (LBL, with 43 students), and Group C (TBL, with 43 students). The learning outcomes were evaluated by a questionnaire and two tests covering basic knowledge of neurology and clinical practice. RESULTS: The practice test scores of Group A were similar to those of Group B, but significantly higher than those of Group C. The theoretical test scores and the total scores of Group A were significantly higher than those of Groups B and C. In addition, 100% of the students in Group A were satisfied with the combination of TBL + LBL. CONCLUSIONS: Our results support our proposal that the combination of TBL + LBL is acceptable to students and produces better learning outcomes than either method alone in neurology clerkships. In addition, the proposed hybrid method may also be suited for other medical clerkships that require students to absorb a large amount of abstract and complex course materials in a short period, such as pediatrics and internal medicine clerkships

RadOnc-GPT: A Large Language Model for Radiation Oncology

This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation oncology patient records and clinical notes from the Mayo Clinic in Arizona. The model employs instruction tuning on three key tasks - generating radiotherapy treatment regimens, determining optimal radiation modalities, and providing diagnostic descriptions/ICD codes based on patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT outputs to general large language model outputs showed that RadOnc-GPT generated outputs with significantly improved clarity, specificity, and clinical relevance. The study demonstrated the potential of using large language models fine-tuned using domain-specific knowledge like RadOnc-GPT to achieve transformational capabilities in highly specialized healthcare fields such as radiation oncology

Artificial General Intelligence for Radiation Oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale